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• A kernel-based feature selection algorithm is proposed, based on primary normalized cross-covariance operator (NOCCO). • A fast version is implemented via combining approximated NOCCO with column recursive method for Moore-Penrose inverse matrix. • Their effectiveness and efficiency are validated using detailed experimental comparison and analysis for nine benchmark data sets. Supervised feature selection (FS) for classification aims at finding a more discriminative subset from original features, to facilitate classifier training, improve classification performance and enhance model interpretability. Primary normalized cross-covariance operator (NOCCO) is a nonlinear kernel-based dependency measure between features and labels, including two inverse matrices, and its approximated version (ANOCCO) is simplified via exploiting linear kernel for features, delta kernel for labels and Moore–Penrose inverse. In this paper, we apply NOCCO and ANOCCO to FS task. According to sequential forward selection, a forward NOCCO-based FS algorithm (i.e., FoNOCCO) is directly implemented via various accelerating strategies, but its computational complexity is extremely high. To this end, we propose its fast version via maximizing ANOCCO (i.e., FoANOCCO), where each candidate feature is evaluated efficiently according to column recursive algorithm for Moore–Penrose inverse. Theoretical analysis shows that evaluating a candidate feature needs a time complexity of O (N 2) in FoANOCCO and O (N 3) in FoNOCCO, where N is the number of instances. On eight small-size high-dimensional benchmark data sets, our detailed experiments illustrate that our two forward FS algorithms are superior to six state-of-the-art FS methods with four baseline classifiers, and FoANOCCO runs 70 times faster than FoNOCCO with a 1.79 % accuracy drop. [ABSTRACT FROM AUTHOR]

Feature combination selection is used in object classification to select complementary features that can produce a powerful combination. One active area of selecting feature combinations is genome-wide association studies (GWAS). However, selecting feature combinations from high-dimensional GWAS data faces a serious issue of high computational complexity. In this paper, a fast evolutionary optimization method named search-history-guided differential evolution (HGDE) is proposed to deal with the problem. This method applies the search history memorized in a binary space partitioning tree to enhance its power for selecting feature combinations. We perform a comparative study on the proposed HGDE algorithm and other state-of-the-art algorithms using synthetic datasets, and later employ the HGDE algorithm in experiments on a real age-related macular degeneration dataset. The experimental results show that this proposed algorithm has superior performance in the selection of feature combinations. Moreover, the results provide a reference for studying the functional mechanisms of age-related macular degeneration. [ABSTRACT FROM AUTHOR]

Feature selection as an essential preprocessing step in multilabel classification has been widely researched. Due to the diversity and complexity of multilabel datasets, some feature selection methods are unstable and yield low predictive accuracy. To address these issues, this paper presents a novel multilabel feature selection method using multilabel ReliefF (ML-ReliefF) and neighborhood mutual information in multilabel neighborhood decision systems. First, to solve the problem of the few available randomly selected samples when searching the nearest samples in ReliefF, the coefficient of difference and the average distance among the nearest similar and heterogeneous samples are introduced to evaluate the differences among the samples, and then the average differences among the similar or heterogeneous samples are defined. Using the Jaccard correlation coefficient, a new formula for updating feature weights is presented. Second, the margin of the sample is studied to granulate all samples under each label, and the concept of the neighborhood is given. By combining algebra with information views, some neighborhood entropy-based uncertainty measures for multilabel classification are investigated, and new neighborhood mutual information is proposed. Furthermore, an optimization objective function is constructed to evaluate the candidate features in multilabel neighborhood decision systems, all the properties are discussed, and the relationships of these measures are established. Finally, an improved ML-ReliefF algorithm is designed for preliminarily eliminating unrelated features to decrease the computational complexity for multilabel classification, and a heuristic forward multilabel feature selection algorithm is developed to remove redundant features and improve classification performance. Experimental results conducted on thirteen multilabel datasets to verify the effectiveness of the proposed algorithms in multilabel neighborhood decision systems are compared with representative methods. [ABSTRACT FROM AUTHOR]

Abstract: MP3, one of the most widely used digital audio formats, provides a high compression ratio with faithful quality. The widespread use enables MP3 audio files to become excellent covers to carry hidden information in audio steganography on the Internet. Our research, however, indicates that there are few steganalysis methods proposed to detect audio steganograms and that steganalysis methods for the information-hiding behavior in MP3 audio are particularly scarce. In this paper we propose a comprehensive approach to steganalysis of MP3 audio files by deriving a combination of features from quantized MDCT coefficients. We design frequency-based subband moment statistical features, accumulative Markov transition features, and accumulative neighboring joint density features on second-order derivatives. We also model the distortion by extracting the shape parameters of generalized Gaussian density from individual frames. Different feature selection algorithms are applied to improve detection accuracy. Signal complexity and modification density are introduced to provide a comprehensive evaluation. Experimental results show that our approach is successful in discriminating MP3 covers and the steganograms generated by the steganographic tool, MP3Stego, in each category of signal complexity, especially for the audio streams with high signal complexities that are generally more difficult to steganalyze. [Copyright &y& Elsevier]

Abstract: The Vertical Bell Laboratories Layer Space–Time (V-BLAST) system using ordered successive interference cancellation (OSIC) detection is a popular system architecture featuring high data rate and moderate computational complexity. The performance of the V-BLAST system can be further improved by exploiting the diversity and closed-loop gain provided by the technique of combined antenna selection (CAS). In this paper, we investigate the design of CAS algorithms with a goal to minimize the systemʼs error rate. As the error rate of a system varies dramatically with the transceiver architecture, the proposed CAS algorithms explicitly take the VBLAST detection structure into account. Despite the optimal CAS can be achieved by exhaustively searching all the transmit and receive antenna subsets, the applicability of this brute-force approach is excessively limited due to its huge computational complexity. To this end, we propose two computationally efficient CAS algorithms by implementing a greedy strategy incrementally or decrementally to fully exploit the recursive structure of mean-square-error matrices between consecutive V-BLAST detection stages. Near optimal error rate performance can therefore be obtained with significantly lower complexity as verified by the extensive computer simulations. [Copyright &y& Elsevier]

Abstract: This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter may be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. [Copyright &y& Elsevier]