Effective gene selection method with small sample sets using gradient-based and point injection techniques

Robust feature selection for microarray data based on multicriterion fusion

Feature selection often aims to select a compact feature subset to build a pattern classifier with reduced complexity, so as to achieve improved classification performance. From the perspective of pattern analysis, producing stable or robust solution is also a desired property of a feature selection algorithm. However, the issue of robustness is often overlooked in feature selection. In this study, we analyze the robustness issue existing in feature selection for high-dimensional and small-sized gene-expression data, and propose to improve robustness of feature selection algorithm by using multiple feature selection evaluation criteria. Based on this idea, a multicriterion fusion-based recursive feature elimination (MCF-RFE) algorithm is developed with the goal of improving both classification performance and stability of feature selection results. Experimental studies on five gene-expression data sets show that the MCF-RFE algorithm outperforms the commonly used benchmark feature selection algorithm SVM-RFE.

Recursive Mahalanobis separability measure for gene subset selection

Mahalanobis class separability measure provides an effective evaluation of the discriminative power of a feature subset, and is widely used in feature selection. However, this measure is computationally intensive or even prohibitive when it is applied to gene expression data. In this study, a recursive approach to Mahalanobis measure evaluation is proposed, with the goal of reducing computational overhead. Instead of evaluating Mahalanobis measure directly in high-dimensional space, the recursive approach evaluates the measure through successive evaluations in 2D space. Because of its recursive nature, this approach is extremely efficient when it is combined with a forward search procedure. In addition, it is noted that gene subsets selected by Mahalanobis measure tend to overfit training data and generalize unsatisfactorily on unseen test data, due to small sample size in gene expression problems. To alleviate the overfitting problem, a regularized recursive Mahalanobis measure is proposed in this study, and guidelines on determination of regularization parameters are provided. Experimental studies on five gene expression problems show that the regularized recursive Mahalanobis measure substantially outperforms the nonregularized Mahalanobis measures and the benchmark recursive feature elimination (RFE) algorithm in all five problems.

Using the patient's questionnaire data to screen laryngeal disorders

This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. By applying the genetic search, the most important questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the questionnaire data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as questionnaire statements is applied. When testing the developed tools on the set of data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.