5,874 research outputs found
Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images
We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when\ud
more features are selected
Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy
In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes.Postprint (published version
Are screening methods useful in feature selection? An empirical study
Filter or screening methods are often used as a preprocessing step for
reducing the number of variables used by a learning algorithm in obtaining a
classification or regression model. While there are many such filter methods,
there is a need for an objective evaluation of these methods. Such an
evaluation is needed to compare them with each other and also to answer whether
they are at all useful, or a learning algorithm could do a better job without
them. For this purpose, many popular screening methods are partnered in this
paper with three regression learners and five classification learners and
evaluated on ten real datasets to obtain accuracy criteria such as R-square and
area under the ROC curve (AUC). The obtained results are compared through curve
plots and comparison tables in order to find out whether screening methods help
improve the performance of learning algorithms and how they fare with each
other. Our findings revealed that the screening methods were useful in
improving the prediction of the best learner on two regression and two
classification datasets out of the ten datasets evaluated.Comment: 29 pages, 4 figures, 21 table
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