smile.feature.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 generation (or constructive induction) studies methods that modify
* or enhance the representation of data objects. Feature generation techniques
* search for new features that describe the objects better than the attributes
* supplied with the training instances.
*
* Many machine learning methods such as Neural Networks and SVM with Gaussian
* kernel also require the features properly scaled/standardized. For example,
* each variable is scaled into interval [0, 1] or to have mean 0 and standard
* deviation 1. Although some method such as decision trees can handle nominal
* variable directly, other methods generally require nominal variables converted
* to multiple binary dummy variables to indicate the presence or absence of a
* characteristic.
*
* 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;