Due to its computational efficiency and low storage requirement, cross-modal hashing (CMH) gains a lot of attention. However, there are still three issues that affect its performance: 1) most existing methods focus on learning the shared semantics between different modalities with the modality-specific semantics ignored; 2) noisy labels may further exacerbate the semantic differences of different modalities during learning; and 3) most existing methods overlook the complementarity between modality-specific labels and semantic features. To address these issues, this work develops a novel CMH method called Dual-Semantic Enhancement Cross-Modal Hashing with Noisy Labels (DSENL). DSENL consists of three parts: (a) Modality-Specific Label Recovery (MSLR) that obtains modality-specific clean labels by applying matrix decomposition with low-rank and sparse constraints to the observed labels; (b) Semantic Preservation under Label Guidance (SPLG) that enhances the quality of recovered labels by using an$l_{2,1}$norm and maintains semantic consistency across modalities by reducing discrepancies among modality-specific labels; and (c) Dual-Semantic Enhancement Learning (DSEL) that integrates both label and sample semantics from modality-specific to enhance the discriminative capability of hash codes. By DSENL, the discriminability of the learned hash codes is improved. Experimental results on four benchmark datasets demonstrate the effectiveness of DSENL. The source code is available athttps://github.com/niuniubit/DSENL.git.

With the rapid growth of multi-modal data, Cross-Modal Hashing (CMH) is widely applied due to its outstanding performance in both search and storage. Nevertheless, there are two issues to be further addressed: (1) most existing methods neglect dynamic learning of the importance of different labels; and (2) many methods fail to purify the consistency of data extracted from different feature spaces. For this purpose, we propose a method called Dynamic Label Correlations and Dual-Semantic Enhancement Learning for Cross-Modal Retrieval (DLCDE) in this study. This method is formed of two parts: Label Semantic Enhancement with Dynamic Label Reconstruction (LSEDLR) and Sample Semantic Enhancement with Consistency Purification and Structure Maintenance (SECPSM). The former first utilizes label-wise self-expression to dynamically explore the latent correlations between different labels and then employs a graph-based manifold regularizer to explore the structural relationships in the transformed label space to enhance label semantics, the latter leverages Hadamard-Product-based Matrix Factorization to enhance the common relationships between samples, thereby enhancing the sample semantics of the latent shared space. Moreover, dual-semantic enhancement learning is achieved by integrating enhanced label semantics and sample semantics in Distance-Distance Difference Minimization (DDDM). Numerous experiments on four benchmark datasets reveal that DLCDE surpasses a number of state-of-the-art CMH methods . The source code for DLCDE is publicly available at https://github.com/Fizzyf/DLCDE .

Domain Adaptation (DA) is used to reduce cross-domain differences between the labeled source and unlabeled target domains. As the existing semantic-based DA approaches mainly focus on extracting consistent knowledge under semantic guidance, they may fail in acquiring (a) personalized knowledge between intra-class samples, and (b) local knowledge of neighbor samples from different categories. Hence, a multi-semantic-granularity and target-sample oriented approach, called Adaptive Graph Learning with Semantic Promotability (AGLSP), is proposed, which consists of three parts: (a) Adaptive Graph Embedding with Semantic Guidance (AGE-SG) that adaptively estimates the promotability of target samples and learns variant semantic and geometrical components from the source and those semantically promotable target samples; (b) Semantically Promotable Sample Enhancement (SPSE) that further increases the discriminability and adaptability of tag granularity by mining the features of intra-class source and semantically promotable target samples with multi-granularities; and (c) Adaptive Graph Learning with Implicit Semantic Preservation (AGL-ISP) that forms the tag granularity by extracting commonalities between the source and those semantically non-promotable target samples. As AGLSP learns more semantics from the two domains, more cross-domain knowledge is transferred. Mathematical proofs and extensive experiments on seven datasets demonstrate the performance of AGLSP.

As social media faces with large amounts of data and multimodal properties, cross-modal hashing (CMH) retrieval gains extensive applications with its high efficiency and low storage consumption. However, there are two issues that hinder the performance of the existing semantics-learning-based CMH methods: 1) there exist some nonlinear relationships, noises, and outliers in the data, which may degrade the learning effectiveness of a model; and 2) the complementary relationships between the label semantics and sample semantics may be inadequately explored. To address the above two problems, a method called robust asymmetric cross-modal hashing retrieval with dual semantic enhancement (RADSE) is proposed. RADSE consists of three parts: 1) cross-modal data alignment (CDA) that applies kernel mapping and establishes a unified linear representation in the neighborhood to capture the nonlinear relationships between cross-modal data; 2) relaxed label semantic learning for robustness (RLSLR) that uses a relaxation strategy to expand label distinctiveness, and leverages$\ell_{2,1}$norm to enhance the robustness of the model against noise and outliers; and 3) dual semantic enhancement learning (DSEL) that learns more interrelationships between samples under the label semantic guidance to ensure the mutual enhancement of semantic information. Extensive experiments and analyses on three popular datasets demonstrate that RADSE outperforms the most existing methods in terms of mean average precision (MAP), precision recall (P–R) curves, and top-N precision curves. In the comparisons of MAP, RADSE improves by an average of 2%–3% in two retrieval tasks.

Due to high computational efficiency and low storage cost, cross-modal hashing retrieval attracts much attention. However, as heterogeneous data from different modalities often have distinct physical meanings and underlying structures, learning encoding with equal length for different modalities may result in an insurmountable semantic gap. In addition, there are still some issues, e.g., how to combine label and sample information to learn hash codes effectively, how to reduce the time consumption caused by computing n × n similarity matrix, and how to effectively solve the complex discrete optimization problem. To overcome the above challenges, this study propose a novel model called Scalable Discrete and Asymmetric Unequal Length Hashing (SDAULH). First, SDAULH constructs a novel hash model that utilizes unequal length encoding schemes to narrow the semantic gap between heterogeneous modalities. Second, SDAULH develops a dual semantic embedding learning scheme, which combines pairwise similarity between label and sample data to generate a more discriminative hash code. Third, SDAULH associates with both hash codes and label information by an asymmetric relaxation strategy. Furthermore, SDAULH solves directly the discrete optimization problem by generating discrete hash codes. Experimental results on four benchmark datasets demonstrate the promising performance of SDAULH.

To accurately retrieve similar objects from different domains, domain adaptive retrieval method is applied to cope with the domain shift problem in information retrieval. However, existing methods still have two problems: a) they fail to filter out low-confidence samples, leading to error accumulation; and b) they ignore the negative effect of domain discrepancy. To address these two issues, we propose an efficient method called Dynamic Confidence Sampling and Label Semantic Guidance Learning (DCS-LSG). First, Dynamic Confidence Sampling (DCS) is employed to dynamically select high-confidence samples from the target domain so as to improve the effectiveness of learning. Second, Label Semantic Guidance (LSG) learning is presented to enhance the label semantics of features during domain adaptive retrieval. In addition, we introduce a Dual-Projection Relaxation (DPR) strategy to learn more effective features on two specific projection spaces. At last, a two-step hashing strategy is used to generate high-quality hash codes. Experiments on multiple cross-domain retrieval datasets demonstrate that the proposed DCS-LSG can achieve a significant performance improvement.

Hashing has attracted widespread attention in the field of supervised cross-modal retrieval due to its advantages in search and storage. However, there are still some issues to be addressed, e.g.: 1) how to effectively combine sample and label semantics to learn hash codes; 2) how to reduce high computational requirements brought by computing a pairwise similarity matrix; and 3) how to effectively solve discrete optimization problems. To cope with them, a fast asymmetric and discrete cross-modal hashing (FADCH) method is proposed in this article. First, matrix factorization is leveraged to collaboratively construct a common semantic subspace between different modalities. Second, semantic consistency is preserved by aligning the common semantic subspace with the semantic representation constructed from labels, which effectively exploits the semantic complementarity of labels and samples. Third, we embed labels into hash codes and keep the correlation between different modal samples by using a pairwise similarity matrix. Fourth, we use an asymmetric strategy with relaxation to associate hash codes with semantic representation, which not only avoids the difficulty of symmetric frame optimization but also embeds more semantic information into the Hamming space. In addition, a strongly orthogonal constraint is introduced to optimize the hash codes. Finally, an effective optimization algorithm is developed to directly generate discrete hash codes while reducing the complexity from$O(n^{2})$to$O(n)$. The experimental results on three benchmark datasets illustrate the superiority of the FADCH method.

To make full use of the resources in production, the orders are usually split, decoupled, and reassembled to series new orders, which often lead to a new complex high-order set of tasks. Traditional processing methods are to first produce some portion of the order, and then manually adjust the rest production plan. Such a method may cause over-production. Also, when tasks are urgent, it is difficult to control the waste rate of emergency raw materials due to quick responses to the urgent requests without careful planning. It is not a trivial work to guarantee efficiency and emergency of production in this case. Therefore, this paper formalizes the high-order set assignment problems (HOTP) by using group role assignment (GRA). Based on GRA, this paper proposes a role negotiation method by using the Hierarchical clustering and Analytic Hierarchy Process (AHP) algorithms. The formalization of HOTP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). Based on the proposed method, decision makers can optimize production resources and assign them in place at one time, minimize the waste and ensure the productivity. The proposed approaches are verified by simulation experiments and demonstrated to be efficient, reasonable and practicable.

Sentiment classification has been attracting increasing attention with the growth of textual data created on the Internet. Text review data covers a wide range of field, and sentiment classification has been widely known as a highly domain-dependent problem. Unfortunately, the existing methods have achieved good results in the domain with a large number of labeled training data. Some researchers apply classifiers learned from source domain to target domain through transfer learning, which still requires the target domain to have enough unlabeled data to learn the similarity between the domains. In this paper, we propose a collaborative domain-specific and global multi-domain sentiment classification approaches with logistic regression. We train a domain-specific sentiment classifier for each source domain, reconstruct the source domain datasets, and train the global sentiment classifiers. Domain-specific sentiment classifier captures domain-specific sentiment features, and global sentiment classifier captures general sentiment knowledge. Finally, taking the output of the first layer as the input of the second layer, a two-level cross-domain sentiment classification model is constructed by logistic regression. Experimental results on benchmark datasets show that the proposed approach can effectively improve the performance of multi-domain sentiment classification and significantly outperform baseline methods.

Unsupervised Domain Adaptation (UDA) has become a basic technology for cross-domain recognition and has received extensive attention in recent years. UDA aims to obtain a classifier for the target domain by learning source instances with different data distributions. However, traditional domain adaptation algorithms cannot effectively explore the manifold structure of data while reducing the distribution differences between domains. To address this problem, this paper proposes a new UDA framework called Joint Discriminative Distribution Adaptation and Manifold Regularization (DDAMR). DDAMR makes full use of the category information and geometric structure of samples in the Grassmann manifold to learn the domain-invariant classifier. Specifically, DDAMR performs discriminative distribution adaptation during dynamic distribution calibration to enhance the discrimination ability of the feature space. In addition, DDAMR introduces manifold regularization that can maintain the proximity relationship of the samples. It can maximize effectively the consistency between the prediction structure of the domain-invariant classifier f and the inherent manifold structure of the sample. A large number of results from cross-domain experiments have demonstrated the effectiveness of our DDAMR algorithm.

Medical resources of physical examination (P.E.) are often insufficient. The gap between providers and demanders are always existing and becoming more and more sensitive in some densely populated areas. If resources of P.E. departments are regarded as nodes, then each path selected by patients will constitute a small world network. Furthermore, with respect to traditional research concentrating on the feature between nodes in network, adaptive collaboration (AC) is in fact an important method to improve the group performance of the whole system in the small world network. Based on these, this paper deals with this kind of problem with respect to the scenario of P.E., which attempts to help decision makers of the health center to schedule limited resources and improve a patient's satisfaction and the system performance. It firstly abstracts a medical examination by Role-Based Collaboration (RBC) and its general model E-CARGO. The adaptive collaboration model is constructed by system states and optimized via series of group role assignments (GRAs), which is a subtask of RBC, and it can be accomplished by linear programming. All the proposed methods are verified by simulation experiments, and the team performance is improved via adaptation of the assignment strategies, which provides a new insight into the small world study.

Human age estimation from face image has drawn increasing research attention due to its many meaningful applications such as demographics analysis and surveillance monitoring. However, most existing methods directly extract age-specific features for age estimation and ignore age-related gender information. In this paper, we propose a simplified deep learning network for age estimation by simultaneously learning aging and potential gender features. Specifically, we first learn the potential gender information from face images. Then, we employ a two-stream convolutional neural network to simultaneously learn and concatenate the aging and gender latent appearance features. Third, we feed the multi-type features into a compact convolution network, named AgeNetwork, to further learn the age-specific features. Finally, we use a deep regression function to estimate the detailed ages. Extensive experimental results demonstrate the promising effectiveness and efficiency of our proposed method in comparison with state-of-the-arts.

Incomplete multi-view clustering has attracted increasing attentions due to its superiority in partitioning unlabeled multi-view data with missing instances in real application. However, most existing methods cannot fully exploit both the view-specific and cross-view relations among data points and ignore the high-order correlations across all views. To address these issues, we propose a novel Incomplete Multi-view Subspace Clustering with Low-rank Tensor (IMSCLT) method, which could be the first tensor-based incomplete multi-view clustering method to the best of our knowledge. Specifically, the subspace representations with low-rank tensor constraint are employed to exploit both the view-specific and cross-view relations among data points and capture the high-order correlations of multiple views simultaneously. In addition, we devise a novel module which can learn a discriminative similarity graph for multi-view learning task by approximating the inner product of the view-specific and common subspace representations. Augmented Lagrangian alternative direction minimization strategy is adopted to solve the proposed IMSCLT. The experiments on several benchmark datasets demonstrate the effectiveness of IMSCLT.

Cross-modal hashing has attracted much attention for cross-modal retrieval and achieved promising performance due to its powerful capacity. Some existing cross-modal hashing methods construct pairwise similarities to represent the relationship of heterogeneous data, which require much computation time and storage space, making them unscalable for large-scale retrieval tasks. In this paper, we propose a novel supervised Discrete Semantic Embedding Hashing (DSEH) for cross-modal retrieval. Specifically, we first learn the common representation of heterogeneous data by embedding the semantic labels into a collective matrix factorization, such that both intra- and inter-modality similarities can be well captured. Then, we learn the hash codes in the discrete space based on the learned common representation via an orthogonal rotation technique. Moreover, we learn the multi-modal hash functions that can efficiently convert out-of-sample instances into unified hash codes. Extensive experimental results on three widely used benchmark databases demonstrate the superiority of the proposed DSEH compared with previous state-of-the-arts.

Oil resources are becoming increasingly scarce. Pure electric vehicles have huge advantages, in terms of energy efficiency and emission reduction. In cities, the locations of required charging stations and the number of required charging piles are determined according to the traffic flow on a road. Unreasonable allocation not only creates safety problems due to high electrical loads, but also increases the cost of the placements. Such allocations will involve the many-to-many (M2M) assignment in the process, which is necessary to establish an optimal model for distributing. Thus, this paper formalizes the charging pile siting problem (CPSP) via the group multirole assignment (GMRA) model, which is one of the most important methods to deal with the M2M problem. Based on GMRA, this paper proposes a role negotiation method by using a spectral clustering K-Means++ Algorithm based on location. The formalization of GMRA makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX) via the Integer Programming (IP). All the proposed approaches are verified by simulation experiments, which have been proved to be efficient, feasible and practicable.

Hashing has been widely studied for cross-modal retrieval due to its promising efficiency and effectiveness in massive data analysis. However, most existing supervised hashing has the limitations of inefficiency for very large-scale search and intractable discrete constraint for hash codes learning. In this paper, we propose a new supervised hashing method, namely, Discrete Semantic Matrix Factorization Hashing (DSMFH), for cross-modal retrieval. First, we conduct the matrix factorization via directly utilizing the available label information to obtain a latent representation, so that both the inter-modality and intra-modality similarities are well preserved. Then, we simultaneously learn the discriminative hash codes and corresponding hash functions by deriving the matrix factorization into a discrete optimization. Finally, we adopt an alternatively iterative procedure to efficiently optimize the matrix factorization and discrete learning. Extensive experimental results on three widely used image-tag databases demonstrate the superiority of the DSMFH over state-of-the-art cross-modal hashing methods.

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods.

Ensemble classifier, b y combining multiple classifiers, can often achieve better performance than single classifiers in intrusion detection. Although some ensemble methods have been used for intrusion detection, most of them directly fuse detection outputs after multiple classifiers a re generated. It potentially reduce the overall performance and flexibility. Aiming at achieving a high-precision intrusion detection model with good generalization performance and robustness, an error-correcting ability based collaborative multi-layer selective classifier ensemble model is proposed in this paper, named ML-SCEM. In the ML-SCEM, a novel multi-layer structure consisting of 5 continuous layers is designed, each layer of which is equivalent to a binary classification. In each layer, an error-correcting based selective classifier ensemble method(SCEM) is used to select the main classifier and error-correcting components from M preselected base classifiers to generate an ensemble classifier suitable for this layer classification category. Furthermore to improve time efficiency a nd detection performance, the original dataset is divided into 3 parts of TCP, UDP and ICMP according to the network protocol, so that the three parts are collaboratively detected. The performance of the proposed ML-SCEM is evaluated and compared on the NSL-KDD dataset. It achieves accuracy of 97.07%, false positive rate of 1.58% and efficiently detects various types of attacks.

Direction information has been intensively investigated for Finger-Knuckle-Print (FKP) recognition. However, most existing direction-based KFP recognition methods are handcrafted, which are heuristic and require too much prior knowledge to engineer them. In this paper, we propose a discriminative direction binary feature learning (DDBFL) method for FKP recognition. We first propose a direction convolution difference vector (DCDV) to better describe the direction information of FKP images. Then, we learn a feature projection to convert the DCDV into binary codes, which are compact for the intra-class samples and more separable for the inter-class samples. Finally, we concatenate the block-wise histograms of the DDBFL codes to form the final descriptor for FKP recognition. Experimental results on the baseline PolyU FKP database demonstrate the competitive performance of the proposed method.

In this paper, a Catboost-based framework is proposed to predict social media popularity. The framework is constituted by two components: feature representation and Catboost training. In the component of feature representation, numerical features are directly used, while categorical features are converted into numerical features by a method of order target statistics in Catboost. Besides, some additional user information is also tracked to enrich the feature space. In the other component, Catboost is adopted as the regression model which is trained by using post-related, user-related and additional user information. Moreover, to make full use of the dataset for model training, a dataset augmentation strategy based on pseudo labels is proposed. This strategy involves in two-stage training. In the first stage, it trains a first-stage model that is used to label the test set as pseudo labeled. In the next stage, a final model is trained based on the new training set that includes original validation set and the pseudo labeled test set. The proposed method achieves the 2nd place in the leader board of the Grand Challenge of Social Media Prediction.

Cybersecurity, especially intrusion detection, is becoming increasingly critical in our daily life. The intrusion detection systems (IDS) have been widely used to prevent disclosure of personal information and detect potentially suspicious attacks. Although many machine learning algorithms have been broadly applied to enhance the performance of IDS, low detection rate and high false alarm rate are still two critical problems. A collaborative and robust intrusion detection model using a novel optimal weight strategy based on Genetic Algorithm (GA) for ensemble classifier is proposed in this paper. Since network data stream can be divided into three categories according to network protocols, detectors are applied in the network protocol separately. All of the detectors can work collaboratively and efficiently. In the proposed model, GA is used to optimize the weight of each base classifier of ensemble classifier. In order to improve features quality, Principal Component Analysis (PCA) is used for dimension reduction and attribute extraction. The NSL-KDD datasets is used to test the effectiveness of the collaborative intrusion detection model. Experimental results show that the proposed model has a higher accuracy and better generalized performance than others in this field.

Nowadays, as the development of mobile communication, it is very important to serve users. Because of the subjectivity of user experience, the data of user experience has deviation. In the paper, a cooperative modeling method based on the improved Support Vector Machine is proposed, which can evaluate the quality of experience by using measurement report. The results of the experiments show that our method is effective to calculate the quality of experience.

Natural disasters can lead to severe losses in human life and property. Because many factors combine in a disaster, such events are difficult to forecast accurately. A cooperative multi-classifier method is proposed in this paper to mine local area meteorological data. The proposed method is verified by the implementation of both base and integration classifiers. Experimental results indicate that our proposed method has higher classification accuracy and faster grouping ability compared with conventional classifiers.

We firstly analyze the method for four attack types, including Probing, DoS (Denial of Service), R2L (Remote to Local) and U2R (User to Root). Based on resource addresses and destination addresses of the network packages, attacks can be divided into four cases, which are respectively one host-one host, one host-many hosts, many hosts-one host and many hosts-many hosts. Specifically, the granular computing method is applied in intrusion detection. A cooperative intrusion detection model is proposed based on granular computing. The construction for an intrusion detection agent is presented.

SBD (Shot Boundary Detection) have great impact on effective browsing and retrieving of the video. It serves as the preliminary step to construct the content of videos. The Spatio-temoral local principle is used to detect shot boundary of videos. Based on careful analysis about the feature of videos, variable thresholds are used in different videos. A cooperative SBD model is proposed in the text. The components of the model are showed. The experimental results reveal the effectiveness and robustness of our method.

Because the network connection information contains nominal and linear attributes, and linear attributes are divided into continuous and discrete attributes, the network connection information is the heterogeneous data. The heterogeneous distance functions are used to cluster data in this paper. The cooperative network intrusion detection based on semi-supervised clustering algorithm is proposed. Firstly, the network data flows are divided into three data flows (TCP flow, UDP flow, and ICMP flow) according to network protocol and are sent to three detection agents. Then every detection agent constructs the detection model using the fuzzy c-means clustering algorithm based on the HVDM (Heterogeneous Value Difference Metric) distance. Finally, revise and verify the detection model by using test data. Simulation experiments are done by using KDD CUP 1999 data set, results show that the method presented here is feasible and efficient.

Based on insertion, Quick-Sort and Merge-Sort algorithms, this paper proposes an improved method about indexing and presents its corresponding parallel algorithm. Introduction of index table increases memory consumption but decreases consumption of record movement in sorting. The experiment demonstrates that executing CPU time of indexingbased sort algorithm is evidently less than that of other sort algorithms. Based on index table and parallel computing, the Merge-Sort algorithm saved the waiting and disposal time in which every two sub-merging sequences are sorted in single processor computer. This obtained better efficiency than the original Merge-Sort algorithm.

Researchers have done lots of work in scan attack detection. Various methods have been proposed. Although these methods can defense some scan attacks from hackers in some degree, there are lots of missing detections and false alerts. Especially current intrusion detection systems are difficult to satisfy the demand of large-scale distributed network. After we carefully research on network topological architecture and scan attack method and mechanism, we find that scan attack always happened at network layer and transport layer. Then we propose a scan detection method based on distributed cooperative model. It is composed of feature-based detection, scenario-based detection and statistic-based detection. The experiment results show that this method has obvious advantages. It can efficiently detect more scan attacks.

This paper discusses that Role-Based Management (RBM) is applied in a Web-based teaching system. A user may have multiple roles. A role may be given to multiple users. Different roles have different privileges. From the implementation, it is found that roles are good mechanisms in designing a learning system.

When a new intrusion means is developed, many intrusion methods can be derived by exchanging the command sequences or by replacing commands with the functionally similar commands, which makes the detection of the developed intrusion very difficult. To overcome this problem, a cooperative intrusion detection model based on scenario is proposed, which is consisted of 5 layers. Topological order, isomorphic transformation and state transition analysis method are applied in the text. For an intrusion case we generate all the possible derived intrusions as an intrusion base. Based on this intrusion base, we present an efficient method to detect such intrusions by using finite automaton. Further, we apply data fusion to analysis suspicious data. A derived intrusion can be seen as an unknown intrusion, in this sense the technique presented in this paper can detect some unknown intrusions.

The source data under the cooperated multi-agent work is always redundant, repetitive, inaccurate, uncompleted or inconsistent, so it is necessary to check, integrate and reduce the data, and convert the data into the evidences according with the internal reasoning mechanism. This paper presents the data pre-processing method and association analysis method for the alarming data.