We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.

We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.

Addressing the challenge of matching queries with the right experts amid temporal-textual inconsistencies, we present a novel approach that combines an attention-based text embedding model with a continuous-time module. This method effectively maps queries to relevant experts by analyzing concept-oriented vectors and user behavior, demonstrating significant effectiveness on StackOverflow and Yahoo datasets.

Finding the most suitable individual to answer a question using brief content has important usages, including the community of question answering systems and online recommender frameworks. However, one must tackle challenges: Disregarding the indispensable noise in short text contents, authors usually answer the input query with mismatched words that can negatively influence the textual relevance. Moreover, many vocabularies imply various alterations. Finally, not every expert is eager to answer an input query given the time constraint, named the reluctance dilemma. To overcome the challenges, we devise a novel embedding approach that constructs context-aware vectors. We then extract the knowledge domains out of the online contextual content. While we track user textual-temporal behavioral patterns via an infinite continuous-time module, we recommend a set of experts pertinent to the given query and willingly provide the response during the expected time. Experimental results on two real-world datasets ofStackOverflowandYahooshow that our online time-sensitive value-wise transformer can achieve higher effectiveness and efficiency versus other trending rivals in online expert recommendation systems. In addition, we empirically experience that Fourier transformers can automatically infer multi-aspect base signals and overpass manual discrete-time models in obtaining time-specific user profiles.

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. First, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Second, traditional text mining methods fail to effectively extract concepts through words and phrases. Third, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using knowledge-bases can make the results biased to the content of the external database and deviate the meaning from the input short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the subgraphs with highly correlated authors from short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. In addition, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.

This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a f uzzy min-max neural network. In the proposed learning algorithm, the flexibility inherent in the fuzzy logic and the computational efficiency of the min-max neural networks are combined in the decision tree learning framework. FMMDT splits the feature space non-linearly based on multiple attributes which provides not only conceptually more insightful splits but also decision trees with smaller size and depth. The decision trees resulted from the FMMDT learning algorithm have a non-traditional architecture, which enables determining the class label of the instances as early as possible. Moreover, FMMDT creates decision trees which are interpretable by the domain expert. It is shown experimentally that the decision trees resulted from the proposed FMMDT learning algorithm achieve the highest accuracy and the lowest size and depth in comparison with C4.5, BFTree, SimpleCart and NBTree on the most commonly used UCI data sets. Moreover, the experiments reveal that FMMDT creates decision trees with stable structure.

One versus all (OVA) decision trees learn k individual binary classifiers, each one to distinguish the instances of a single class from the instances of all other classes. Thus OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each one to discriminate among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and hence high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Second, OVA's classification accuracy is adversely affected by the imbalanced class distribution in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating and higher classification accuracy than many existing popular data stream classification algorithms.

Learning of drifting concepts has recently received great attention. This is mostly due to its capability of modeling natural phenomena more realistically, provided that it is done effectively. So far incremental and window based ensemble learning have been widely used as the two most effective methods for tracking concept changes. In windowing methods, the most recent samples are considered relevant as training examples to be fed to the underlying "base" learning algorithm, as well as for evaluating its accuracy. Here we present a cellular automata- (CA) based approach which improves the current widow- based relevance criterion by adding neighborhood distance as another relevance measure for data samples. Emergence of new samples in the stream affects their "nearby" samples' chance of being considered relevant for the learning task. Experiments show that a good choice of local rules for CA can reduce the concept convergence time considerably and increase model robustness to noise; thus presenting a more accurate stream-learning.

Abstract: The purpose of the intrusion detection system (IDS) database is to detect transactions that access data without permission. This paper proposes a novel approach to identifying malicious transactions. The approach concentrates on two aspects of database transactions: (1) dependencies among data items and (2) variations of each individual data item which can be considered as time‐series data. The advantages are threefold. First, dependency rules among data items are extended to detect transactions that read or write data without permission. Second, a novel behaviour similarity criterion is introduced to reduce the false positive rate of the detection. Third, time‐series anomaly analysis is conducted to pinpoint intrusion transactions that update data items with unexpected pattern. As a result, the proposed approach is able to track normal transactions and detect malicious ones more effectively than existing approaches.

In this paper, we present a genetically induced oblique decision tree algorithm. In traditional decision tree, each internal node has a testing criterion involving a single attribute. Oblique decision tree allows testing criterion to consist of more than one attribute. Here we use genetic programming to evolve and find an optimal testing criterion in each internal node for the set of samples at that node. This testing criterion is the characteristic function of a relation over existing attributes. We present the algorithm for construction of the oblique decision tree. We also compare the results of our proposed oblique decision tree with the one of C4.5 algorithm.