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• package-info.java

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smile.feature.selection.package-info Maven / Gradle / Ivy

/*
 * Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile.  If not, see .
 */

/**
 * Feature selection. Feature selection is the technique of selecting a subset
 * of relevant features for building robust learning models. By removing most
 * irrelevant and redundant features from the data, feature selection helps
 * improve the performance of learning models by alleviating the effect of
 * the curse of dimensionality, enhancing generalization capability, speeding
 * up learning process, etc. More importantly, feature selection also helps
 * researchers to acquire better understanding about the data.
 * 

 * Feature selection algorithms typically fall into two categories: feature
 * ranking and subset selection. Feature ranking ranks the features by a
 * metric and eliminates all features that do not achieve an adequate score.
 * Subset selection searches the set of possible features for the optimal subset.
 * Clearly, an exhaustive search of optimal subset is impractical if large
 * numbers of features are available. Commonly, heuristic methods such as
 * genetic algorithms are employed for subset selection.
 *
 * @author Haifeng Li
 */
package smile.feature.selection;