Abstract
BackgroundExisting feature selection methods typically do not consider prior knowledge in the form of structural relationships among features. In this study, the features are structured based on prior knowledge into groups. The problem addressed in this article is how to select one representative feature from each group such that the selected features are jointly discriminating the classes.The problem is formulated as a binary constrained optimization and the combinatorial optimization is relaxed as a convex-concave problem, which is then transformed into a sequence of convex optimization problems so that the problem can be solved by any standard optimization algorithm. Moreover, a block coordinate gradient descent optimization algorithm is proposed for high dimensional feature selection, which in our experiments was four times faster than using a standard optimization algorithm.ResultsIn order to test the effectiveness of the proposed formulation, we used microarray analysis as a case study, where genes with similar expressions or similar molecular functions were grouped together. In particular, the proposed block coordinate gradient descent feature selection method is evaluated on five benchmark microarray gene expression datasets and evidence is provided that the proposed method gives more accurate results than the state-of-the-art gene selection methods. Out of 25 experiments, the proposed method achieved the highest average AUC in 13 experiments while the other methods achieved higher average AUC in no more than 6 experiments.ConclusionA method is developed to select a feature from each group. When the features are grouped based on similarity in gene expression, we showed that the proposed algorithm is more accurate than state-of-the-art gene selection methods that are particularly developed to select highly discriminative and less redundant genes. In addition, the proposed method can exploit any grouping structure among features, while alternative methods are restricted to using similarity based grouping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0954-4) contains supplementary material, which is available to authorized users.
Highlights
Existing feature selection methods typically do not consider prior knowledge in the form of structural relationships among features
(1) We formulate the feature selection problem in order to select a representative feature from each group simultaneously and jointly as convex-concave optimization, which is transformed into a sequence of convex optimization problems that can be solved using any standard efficient optimization algorithm; (2) We develop a block coordinate gradient descent (BCGD) algorithm that is four times faster than any standard optimization algorithm for the proposed feature selection method; (3) The experimental results show evidence of the efficiency and scalability of the proposed algorithm
It chooses instances randomly and changes the weights of the feature relevance based on the nearest neighbor so that it gives more weights to features that discriminate the instance from neighbors of different classes; (3) Maximum Relevance Minimum Redundancy (mRMR) ranks the features according to the minimal-redundancy-maximal-relevance criteria [17, 36], which is based on mutual information; (4) STBIP formulates the feature selection problem as a quadratic objective function to select m features with maximal discriminative power and minimal redundancy [19], where the redundancy among features is computed based on Pearson correlation
Summary
Existing feature selection methods typically do not consider prior knowledge in the form of structural relationships among features. The problem addressed in this article is how to select one representative feature from each group such that the selected features are jointly discriminating the classes. The objective of supervised feature selection methods is to select a discriminative but concise list of features among a possibly large set of features in order to differentiate between classes. The requirement is to select one feature from each group such that all features are jointly discriminative. This problem exists in many applications (see Additional file 1 for more details)
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