Unsupervised anomaly detection has attracted considerable attention in complex data environments due to its independence from costly labeled data. Among various approaches, subspace sampling-based ensemble methods, such as IForest, have been widely adopted for their simplicity and computational efficiency. However, these methods typically operate under single granularity, which limits the diversity of subspaces and hinders the ability to capture hierarchical structures and complex patterns in the data. Moreover, they often overlook uncertainty information such as fuzziness among samples, which constrains their capacity to model complex relationships. To address these limitations, this paper proposes a method called Granular-Ball Subspace-based Fuzzy Neighborhood Anomaly Detector (GSFAD). The proposed method integrates granular-ball subspace ensemble learning with a fuzzy computing framework, achieving a balance between computational efficiency and the ability to model multi-granularity fuzzy structures. Specifically, the algorithm begins by performing multi-granularity aggregation with granular-balls to cover the origin data. Then, regions potentially containing anomalies are filtered out based on granular-ball characteristics before sampling. Building on this, multiple granular-ball subspaces are constructed via repeated sampling, and fuzzy relations between granular-balls are computed within each subspace. Finally, the anomaly score of each sample is assessed by fusing the fuzzy neighborhood information across all subspaces. Experimental results on 20 benchmark datasets demonstrate that GSFAD consistently outperforms existing subspace sampling methods that operate under a single granularity. In addition, it achieves superior performance compared to 15 state-of-the-art anomaly detection techniques. The code is publicly available online athttps://github.com/Caspar-lab/GSFAD.

With the successful application of granular computing in anomaly detection, a variety of tools including fuzzy information entropy can achieve superior detection results. However, fuzzy information entropy calculates fuzzy similarity through a global strategy, ignoring the local information in the data. To address this deficiency, this paper constructs a fuzzy$k$NN entropy theory and applies it to identify anomalies. Firstly, fuzzy$k$-similarity and fuzzy$k$NN are defined, and$k$NN entropy theory and the related information-theoretic metrics are proposed. Then, the relevant definitions and propositions of fuzzy$k$NN entropy, fuzzy$k$-joint entropy, fuzzy$k$-conditional information entropy, as well as fuzzy$k$-mutual information are elaborated. Based on the proposed theory, an anomaly detection model is constructed. At first, the fuzzy$k$-similarity relation matrix is constructed based on the fuzzy$k$-similarity in the proposed theory, and the relative fuzzy$k$NN entropy is calculated. Based on the relative fuzzy$k$NN entropy, the fuzzy$k$-relation anomaly degree is defined to characterize the anomaly intensity of fuzzy$k$NN information granules. Then, the anomaly factor based on fuzzy$k$NN entropy is built to represent the anomaly degree of data objects. Finally, the corresponding Fuzzy$k$NN Entropy-based Anomaly Detection algorithm (F$k$EAD) is designed. Comparative experiments are conducted with 11 state-of-the-art anomaly detection methods on thirty public datasets. The results reveal that the proposed method achieves better performance.

In information systems lacking decision-making information, effectively leveraging fuzzy rough sets for outlier detection in complex data is challenging, especially in capturing inherent uncertainty and multi-granularity characteristics to construct discriminative outlier scores. However, existing fuzzy rough sets-based outlier detection methods often suffer from three key limitations: (1) Local data distributions are often ignored when calculating fuzzy relation matrices, resulting in inaccurate fuzzy similarity representations; (2) Use of all objects in fuzzy upper and lower approximations can weaken noise resistance and increase computational complexity; (3) Single-granularity data processing reduces efficiency and may fail to capture the multi-granularity nature of data, thereby limiting the adaptability of these methods in complex data environments. To address these issues, we propose to fusesNatural neighbor fuzzy approximations withGranular-ball representation forOutlierDetection (NGOD), which integrates the multi-granularity granular-ball representation and fuzzy rough sets to improve the effectiveness and robustness of unsupervised outlier detection. Specifically, we first define a local distribution-aware fuzzy relation, enabling more discriminative similarity calculations between samples. To improve the effectiveness and robustness of fuzzy upper and lower approximations, we propose a multi-granularity natural neighbor fuzzy approximation model, which effectively utilizes the inherent uncertainty and local abnormal information of data in approximations. Moreover, by introducing natural neighbors, NGOD can adaptively capture local abnormal information in the data without setting neighborhoods manually. Finally, the outlier factors of each sample are calculated in NGOD to measure their outlier degrees. Extensive experiments on diverse datasets demonstrate that NGOD outperforms state-of-the-art methods, validating its superior performance and adaptability. The NGOD code and associated datasets are publicly available athttps://github.com/Mxeron/NGOD.

Multi-view unsupervised feature selection (MV-UFS) has recently gained significant attention as an effective technique for handling high-dimensional multi-view data. Despite recent progress, three flaws are present in most existing MV-UFS methods. First, they predominantly pay attention to the feature dimensionality while neglecting sample size considerations, typically resulting in cubic time complexity concerning sample size (i.e.$\mathcal {O}(n^{3})$) that hinders them in real-world applications. Second, existing methods primarily focus on the relationships between pairwise views, failing to leverage the complex interactions among multiple views effectively. Third, they generally overlook crucial graph semantic information during graph fusion. An ingenious MV-UFS method is presented in this paper, namedJoint Information Interaction and Semantic Fusion for Multi-View Unsupervised Feature Selection (JIISF), to overcome these limitations. Specifically, JIISF devises a multi-view double reduction learning framework that projects original data into a low-dimensional embedding space and dynamically picks out representative anchors, eliminating redundant information while constructing compact view-specific anchor graphs. Meanwhile, JIISF presents a cross-view information interaction scheme, which enables comprehensive information interaction among multiple views rather than being limited to pairwise views. A semantic alignment fusion strategy is presented to learn a diverse, consistent anchor graph with an explicit semantic alignment module, which can preserve diversity while maintaining consistency. Finally, an alternating optimization algorithm is devised to resolve the proposed JIISF, and experimental results on real-life datasets validate its advantage compared with some state-of-the-art UFS and MV-UFS methods.

Graph-based feature selection methods can capture the most discriminative subset of features in multi-label data and have made great strides in recent years. However, most existing methods have three drawbacks: (1) Only binary pairwise relationship is difficult to capture latent structural information in high-dimensional data fully. (2) Using only one distance metric cannot accurately mine the similarity relationship between instances. (3) Ignore the problem that some of the non-obvious labels are missing when labels are abundant. Therefore, this paper proposes a graph diffusion with dual-distance metrics for missing multi-label feature selection named GDMMFS. GDMMFS uses both Euclidean and cosine distances to explore instance correlations and then uses diffusion techniques to transform binary relationship into quaternary relationship, thus obtaining a stable similarity graph. Moreover, GDMMFS utilizes adaptive graphs to learn latent structure information to improve the model’s immunity to interference. Finally, the ℓ 2 , 1 -norm is used as a sparse constraint to guide the weight matrix to capture the most discriminative subset of features. To further consider the possibility of missing label data, GDMMFS constructed a logic matrix to guide the numerical labels in recovering the missing information. Comparison experiments with state-of-the-art related algorithms demonstrate the superiority of the proposed algorithm, and ablation experiments demonstrate the effectiveness of graph diffusion with two-distance metrics.

Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS. However, this may introduce additional regularization terms, and it is difficult to control the sparsity of the projection matrix well. Moreover, such methods may seriously destroy the original feature structure in the embedding space. Instead, avoiding projecting the original data into the low-dimensional embedding space and identifying features directly from the raw features that perform well in the process of making the data show a distinct cluster structure is a feasible solution. Inspired by this, this paper proposes a model called A General Adaptive Unsupervised Feature Selection with Auto-weighting (GAWFS), which utilizes two techniques, non-negative matrix factorization, and adaptive graph learning, to simulate the process of dividing data into clusters, and identifies the features that are most discriminative in the clustering process by a feature weighting matrix Θ. Since the weighting matrix is sparse, it also plays the role of FS or a filter. Finally, experiments comparing GAWFS with several state-of-the-art UFS methods on synthetic datasets and real-world datasets are conducted, and the results demonstrate the superiority of the GAWFS.

Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semisupervised methods typically treat data as purely numerical and in a deterministic manner, thereby neglecting the heterogeneity and uncertainty inherent in complex, real-world datasets. This article introduces a label-informed outlier detection method for heterogeneous data based on Granular Computing and Fuzzy Sets, namely Granule Density-based Outlier Factor (GDOF). Specifically, GDOF first employs label-informed fuzzy granulation to effectively represent various data types and develops granule density for precise density estimation. Subsequently, granule densities from individual attributes are integrated for outlier scoring by assessing attribute relevance with a limited number of labeled outliers. Experimental results on various real-world datasets show that GDOF stands out in detecting outliers in heterogeneous data with a minimal number of labeled outliers. The integration of Fuzzy Sets and Granular Computing in GDOF offers a practical framework for outlier detection in complex and diverse data types.

Outlier Detection (OD) has attracted extensive research due to its application in many fields. The idea of neighborhood computing is one of the widely used methods in outlier analysis. Nevertheless, these methods mainly use certainty strategies to model outlier detection, so they cannot effectively handle the fuzzy information in the dataset. Moreover, they mainly focus on dealing with outlier detection in numerical data and cannot effectively find outliers in mixed-attribute data. Fuzzy information granulation theory is an effective granular computing model that allows objects to belong to a set to a certain extent (i.e., membership degree), which makes it possible to better handle uncertainty problems such as fuzziness. In this work, we propose an outlier detection model based on fuzzy neighborhoods. First, a hybrid fuzzy similarity is constructed to granulate the set of objects to form fuzzy information granules. Second, the fuzzy$k$-nearest neighbor is defined to describe the fuzzy local information. Then, the fuzzy neighborhood density is defined to indicate the degree of aggregation of each object. The smaller the fuzzy neighborhood density of an object, the more likely it is to be an outlier. Based on this idea, the fuzzy neighborhood deviation degree is defined to quantify the degree of outliers of objects. Finally, the fuzzy deviation degree on the set of conditional attributes is constructed to indicate the outlier scores of objects. Experimental comparisons with state-of-the-art methods show that the proposed method has a significant improvement on the AUC index and applies to three types of data.

Traditional multilabel feature selection (MFS) typically relies on pre-computing global information within the feature space. However, in real-world applications, features are dynamically generated and continuously arrive over time, known as streaming features, rendering many existing approaches ineffective. Some MFS methods for streaming features have been developed, several challenges persist: (1) Previous research often uses certain strategies to model streaming feature evaluation, failing to process fuzzy information effectively; (2) The maximum correlation between features and class is emphasized, while inter-class separability is ignored, leading to inaccurate feature evaluation; (3) The continuous influx of streaming features brings the dynamics and unknowns to data distribution, has been largely overlooked in previous work; (4) Streaming feature selection requires immediate feedback on newly arriving features, posing challenges to the algorithm's real-time responsiveness. Motivated by these observations, this paper introduces a novel online MFS strategy for streaming features. First, the weighted manifold distance is designed, and the fuzzy manifold similarity learning strategy is formalized to analyze the instance relationships of unknown distribution. Second, the fuzzy manifold intra-class correlation and inter-class separability are devised to quantify feature discriminability. Finally, a novel multilabel streaming feature analysis framework is established, with feature discriminability as the guiding factor. Incoming features are categorized as weakly relevant, strongly relevant, or redundant, culminating in generating a reliable feature selection subset. Extensive experiments on fifteen public datasets demonstrate that our algorithm achieves competitive performance compared to nine state-of-the-art offline and online algorithms.

Existing density-based outlier detection methods process data at the single-granularity level of individual samples, requiring pairwise distance calculations between all samples and exhibiting high sensitivity to noise. The single-granularity-based processing paradigm fails to mine the information at multiple levels of granularity in data, and most of these methods ignore the potential uncertainty information in data, such as fuzziness, resulting in an inability to effectively detect potential outliers in data. As a novel granular computing method, Granular-Ball Computing (GBC) is characterized by its multi-granularity and robustness, which makes it able to make up for the above drawbacks well. In this study, we propose local Granular-Ball Density-based Outlier (GBDO) detection to improve the performance of the density-based methods. In GBDO, we first identify the $k\text {-}$ similarity Granular-Ball (GB) neighborhoods of each GB via the fuzzy relations among them. Subsequently, the local reachability similarity density of the GBs is calculated through the reachability similarity we defined. Finally, the local GB outlier factors of the samples are calculated based on the local reachability similarity density of the GBs. We adopt a multi-granularity processing paradigm using GBs as the basic units, which reduces computational complexity and improves robustness to noisy data by leveraging the multi-granularity nature of GBs. The experimental results demonstrate the effectiveness of GBDO by comparing it with state-of-the-art methods. The source code and datasets are publicly available at https://github.com/Mxeron/GBDO.

Anomaly detection is a significant area of discovering knowledge that has shown success in the areas of fraud detection, cyber security, and medical diagnostics. The kernelized fuzzy-rough set is a key extension model in rough computing. It inherits the advantages of kernel function and fuzzy-rough set and can handle uncertain information in data more effectively. However, existing kernelized fuzzy-rough set models construct upper and lower approximation sets mainly from decision attribute data which are not available to unlabeled data. In addition, to the best of our knowledge, studies related to the use of kernelized fuzzy upper and lower approximation sets for constructing effective anomaly detection models have not yet been reported. Based on these observations, this paper constructs a kernelized fuzzy-rough set model for unlabeled data and proposes a kernelized fuzzy-rough anomaly detection (KFRAD) method. Specifically, we first optimize the kernel function to compute the fuzzy relation matrix of attribute subsets to construct the fuzzy approximation space. Then, the definition of kernelized fuzzy approximation accuracy for unlabeled data is given. Then, the granule anomaly extent is determined based on the kernelized fuzzy approximation accuracy. Finally, the granule anomaly extents and the corresponding weight values are integrated to compute the kernelized fuzzy-rough anomaly scores of the objects. On the basis of the above detection ideas, we design the KFRAD algorithm and experimentally compare it with mainstream detection algorithms. The analysis results show that the proposed algorithm has better performance. The code is publicly available online athttps://github.com/wwwuyan/KFRAD.

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of outlier detection, the classical approaches from distances apply to numerical data rather than nominal data, while the recent methods on basic rough sets deal with nominal data rather than numerical data. Aiming at wide outlier detection on numerical, nominal, and hybrid data, this paper investigates three-way neighborhood characteristic regions and corresponding fusion measurement to advance outlier detection. First, neighborhood rough sets are deepened via three-way decision, so they derive three-way neighborhood structures on model boundaries, inner regions, and characteristic regions. Second, the three-way neighborhood characteristic regions motivate the information fusion and weight measurement regarding all features, and thus, a multiple neighborhood outlier factor emerges to establish a new method of outlier detection; furthermore, a relevant outlier detection algorithm (called 3WNCROD) is designed to comprehensively process numerical, nominal, and mixed data. Finally, the 3WNCROD algorithm is experimentally validated, and it generally outperforms 13 contrast algorithms to perform better for outlier detection.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection. In this article, the NRS model as a unified framework is used to design a feature selection method to handle categorical, numerical, and heterogeneous data. First, the concept of neighborhood combination entropy (NCE) is presented. It can reflect the probability of pairs of the neighborhood granules that are probably distinguishable from each other. Then, the conditional neighborhood combination entropy (cNCE) based on NCE is proposed under the condition of considering decision attributes. Moreover, some properties and relationships between cNCE and NCE are derived. Finally, the functions of inner and outer significances are constructed to design a feature selection algorithm based on cNCE (FScNCE). The experimental results show the effectiveness and superiority of the proposed algorithm.

Most traditional blind source separation (BSS) methods are based on the basic assumption that source signals are mutually independent. However, many signals in the real world are often interrelated to each other. In this paper, a novel BSS method is proposed for dependent signals which only relies on the relaxed sparsity of sources. Under this constraint, the column vector of high-dimensional observed signals would be distributed in a specific subspace. According to the representation relation in time-frequency (TF) domain, each column vector of observed signal is uniquely clustered into the subspace, to which it belongs. Furthermore, the estimation of source signals is calculated by the inverse matrix or the pseudo-inverse matrix of the mixing matrix. It is also demonstrated mathematically and experimentally that under relaxed sparsity conditions, proposed subspace clustering methods is promising and hopeful to solve the dependent BSS (DBSS) problems.

Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data. Therefore, to make up for these two deficiencies, a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed. First, fuzzy multineighborhood granules are constructed to better adapt to the data distribution. Second, feature multicorrelations (i.e., relevancy, redundancy, interactivity, and complementarity) are considered and defined comprehensively using fuzzy multigranularity uncertainty measures. Next, the features with interactivity and complementarity are mined by the forward iterative selection strategy. Finally, compared with the benchmark approaches on several datasets, the experimental results show that ICFS_FmNRS effectively improves the classification performance of feature subsets while reducing the dimension of feature space.

Most extant feature selection works neglect interactive features in the form of groups, leading to the omission of some important discriminative information. Moreover, the prevalence of data with uncertainty, fuzziness, and noise poses a certain obstacle to feature selection. Driven by these two issues, a Feature Grouping and Selection approach in Robust Fuzzy Rough Approximation Space using graph theory (FGS-RFRAS) is proposed in this study. First, a robust fuzzy rough approximation space is constructed by a neighborhood adaptive$\beta$-precision fuzzy rough set model to enhance the robustness and antinoise ability of the fuzzy rough set model. Second, uncertainty measures in robust fuzzy rough approximation space are defined to analyze the interactivity and redundancy of pairwise features on graph structure. Then, a strategy ofInteractive Retainment, Weakly Correlated Removal, and Max-Dependent Selectionis devised to guide feature grouping and selection. Experiments are performed on 21 datasets to evaluate the performance of FGS-RFRAS and demonstrate its significance. The robustness test indicates that it is antinoise for mislabeling.

In practice, it is common that there will be the same decision results under different scale conditions. Therefore, knowledge representation based on a single scale feature framework is far from meeting the needs of practical applications. Based on this, multiscale data have received extensive attention. Feature selection is an important application of fuzzy multigranularity data analysis model. The existing multiscale fuzzy granulation-based feature selection methods remove redundant or irrelevant features by selecting the optimal scale. However, this will lose the information corresponding to the remaining scale fuzzy granules, which will affect the classification results or learning tasks. Inspired by this, multiscale fuzzy entropy is defined to fuse the granule information at different scales, and applied to feature selection. First, the feature with maximum multiscale fuzzy mutual information is first selected. Then, the most significant features are gradually selected by evaluation metric that simultaneously considers the redundancy, relevance, and complementary. A multiscale fuzzy entropy-based feature selection algorithm by means of this evaluation index is further designed. Finally, the proposed method is compared with some state-of-the-art methods. The experimental results show that the proposed algorithm has higher reduction efficiency than the comparison algorithms.

The performance of multilabel learning depends heavily on the quality of the input features. A mass of irrelevant and redundant features may seriously affect the performance of multilabel learning, and feature selection is an effective technique to solve this problem. However, most multilabel feature selection methods mainly emphasize removing these useless features, and the exploration of feature interaction is ignored. Moreover, the widespread existence of real-world data with uncertainty, ambiguity, and noise limits the performance of feature selection. To this end, our work is dedicated to designing an efficient and robust multilabel feature selection scheme. First, the distribution character of multilabel data is analyzed to generate robust fuzzy multineighborhood granules. By exploring the classification information implied in the data under the granularity structure, a robust multilabel$k$-nearest neighbor fuzzy rough set model is constructed, and the concept of fuzzy dependency is studied. Second, a series of fuzzy multineighborhood uncertainty measures in$k$-nearest neighbor fuzzy rough approximation spaces are studied to analyze the correlations of feature pairs, including interactivity. Third, by investigating the uncertainty measure between feature and label, between features, multilabel data is modeled as a complete weighted graph. Then, these vertices are assessed iteratively to guide the assignment of feature weights. Finally, a graph structure-based robust multilabel feature selection algorithm (GRMFS) is designed. The experiments are conducted on 15 multilabel datasets. The results verify the superior performance of GRMFS as compared with nine representative feature selection methods.

The main goal of most information quality (IQ)-based measures is to combine data provided by multiple information sources to enhance the quality of information essential for decision makers to perform their tasks. However, there is few work to fuse multisource information from the perspective of possibility distribution (PD) and use IQ as the evaluation criteria for feature selection. The PD is one of important concepts in the possibility theory, which is a generally acknowledged method for describing a kind of uncertain knowledge. In this article, we propose a novel representation model of PDs based on FMs, namely, a possibility distribution information system (PDIS). Then, several IQ measures are defined in the PDIS, including Gini entropy, compatibility, conflict, credibility, and separability degrees. In view of this, a minimal-separability-minimal-uncertainty-based unsupervised feature selection algorithm (UmSMU) is designed. The proposed UmSMU can sufficiently fuse multiple possibilistic information. Meanwhile, the selected features maintain as much information as possible while minimizing the uncertainty of information. The experimental results show that the proposed algorithm performs well, especially when it comes to selecting fewer features and improving performance.

Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data. There are few studies on the outlier detection of mixed attribute data. In this article, we introduce fuzzy rough sets (FRSs) to deal with the problem of outlier detection in mixed attribute data. Since the outlier detection model of the classical rough set is only applicable to the categorical attribute data, we use FRS to generalize the outlier detection model and construct a generalized outlier detection model based on fuzzy rough granules. First, the granule outlier degree (GOD) is defined to characterize the outlier degree of fuzzy rough granules by employing the fuzzy approximation accuracy. Then, the outlier factor based on fuzzy rough granules is constructed by integrating the GOD and the corresponding weights to characterize the outlier degree of objects. Furthermore, the corresponding fuzzy rough granules-based outlier detection (FRGOD) algorithm is designed. The effectiveness of the FRGOD algorithm is evaluated through experiments on 16 real-world datasets. The experimental results show that the algorithm is more flexible for detecting outliers and is suitable for numerical, categorical, and mixed attribute data.

Aiming at the problem of effectively selecting relevant features from heterogeneous data without decision, a novel feature selection approach is studied based on fuzzy mutual information in fuzzy rough set theory. First, the fuzzy relevance of each feature is defined by using fuzzy mutual information, and then, the fuzzy conditional relevance is further given. Next, the fuzzy redundancy is defined by using the difference between the fuzzy relevance and the fuzzy conditional relevance. Thereby, the evaluation index of the feature importance is obtained by using the idea of unsupervised minimum redundancy and maximum relevance. Finally, a fuzzy-mutual-information-based unsupervised feature selection algorithm is designed to select feature sequences. Extensive experiments are conducted on public datasets, and six unsupervised feature selection algorithms are compared. The selected features are evaluated by classification, clustering, and outlier detection methods. Experimental results show that the proposed algorithm can select fewer heterogeneous features to maintain or improve the performance of learning algorithms.

Outlier detection is widely used in many fields, such as intrusion detection, credit card fraud detection, medical diagnosis, and so on. Existing outlier detection algorithms are mostly designed for dealing with numeric or categorical attributes. However, data usually exist in the form of mixed attributes in real-world applications. In this article, we propose a novel mixed attribute outlier detection method based on multigranulation relative entropy by employing the neighborhood rough set. First, the neighborhood system is constructed by optimizing the mixed distance metric and the radius of the statistical value. Second, the neighborhood entropy is introduced as an uncertainty measure of data. Furthermore, the three kinds of multigranulation relative entropy-based matrices are defined by three kinds of attribute sequences, and the multigranulation relative entropy-based outlier factor is integrated to indicate the outlier degree of every object. Based on the proposed outlier detection model, the corresponding algorithm is designed. Finally, the proposed algorithm is compared with other nine algorithms through experiments on public data. The experimental results show that the proposed technique is adaptive and effective.