Elements of information theory
Elements of information theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle Swarms for Feature Extraction of Hyperspectral Data
IEICE - Transactions on Information and Systems
Advances in Feature Selection with Mutual Information
Similarity-Based Clustering
Information Sciences: an International Journal
Fast kernel conditional density estimation: A dual-tree Monte Carlo approach
Computational Statistics & Data Analysis
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This paper presents a supervised feature selection method applied to regression problems. The selection method uses a Dissimilarity matrix originally developed for classification problems, whose applicability is extended here to regression and built using the conditional mutual information between features with respect to a continuous relevant variable that represents the regression function. Applying an agglomerative hierarchical clustering technique, the algorithm selects a subset of the original set of features. The proposed technique is compared with other three methods. Experiments on four data-sets of different nature are presented to show the importance of the features selected from the point of view of the regression estimation error (using Support Vector Regression) considering the Root Mean Squared Error (RMSE).