A novel method for microarray data classification is proposed in this letter. In this scheme, the sequential floating forward selection (SFFS) technique is used to select the independent components of the DNA microarray data for classification. Experimental results show that the method is efficient and feasible. r 2006 Elsevier B.V. All rights reserved.

In this work the principle of homogeneity between labels and data clusters is exploited in order to develop a semi-supervised Feature Selection method. This principle permits the use of cluster information to improve the estimation of feature relevance in order to increase selection performance. Mutual Information is used in a Forward-Backward search process in order to evaluate the relevance o...

We discuss sparse support vector machines (SVMs) by selecting the linearly independent data in the empirical feature space. First we select training data that maximally separate two classes in the empirical feature space. As a selection criterion we use linear discriminant analysis in the empirical feature space and select training data by forward selection. Then the SVM is trained in the empir...

Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; ...

background: numerous studies used microarray gene expression data to extract metastasis-driving gene signatures for the prediction of breast cancer relapse. however, the accuracy and generality of the previously introduced biomarkers are not acceptable for reliable usage in independent datasets. this inadequacy is attributed to ignoring gene interactions by simple feature selection methods, due...

Ensemble learning constitutes one of the main directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. One technique, which proved to be effective for constructing an ensemble of diverse classifiers, is the use of feature subsets. Among different approaches to ensemble feature selection, genetic search was s...

Feature selection helps to focus resources on relevant dimensions of input data. Usually, reducing the input dimensionality to the most informative features also simplifies subsequent tasks, such as classification. This is, for instance, important for systems operating in online mode under time constraints. However, when the training data is of limited size, it becomes difficult to define a sin...

Mutual information between a target variable and a feature subset is extensively used as a feature subset selection criterion. This work contributes to a more thorough understanding of the evolution of the mutual information as a function of the number of features selected. We describe decreasing returns and increasing returns behavior in sequential forward search and increasing losses and decr...