The approaches to the subject include: genetic algorithm for optimal feature subset selection; k-fold cross-validation method for error rate evaluation and BP neural network for fault classification.

As optimal feature subset selection ( OFSS) is an NP-hard Problem, it is of realistic significance to find an approximative algorithm.

指出最优特征子集选择问题（OFSS）是个NP-Hard问题，寻找一个近似算法具有现实意义。

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After analyzing problems of the feature subset selection in the field of data mining, a new algorithm of feature subset selection based on the improved genetic algorithm is proposed.

对数据挖掘中的特征子集选择问题进行分析，提出了基于改进遗传算法的特征子集选择算法。

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The feature subset selection is an important problem in machine learning, but the optimal feature subset selection is proves to be a NP hard one.

特征子集选择问题是机器学习的重要问题.而最优特征子集的选择是NP困难问题，因此需要启发式搜索指导求解。

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And it introduces some algorithms of decision tree learning such as ID3, C4.5 and feature subset selection of Inductive Learning.

介绍了归纳学习中的决策树学习算法如ID3、C4.5和特征子集选择问题。

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OFFSS ( Optimal Fuzzy-Valued Feature Subset Selection) is an optimal feature subset selection based on fuzzy-valued extension matrix.

最优模糊特征子集选取OFFSS（Optimal Fuzzy-Valued Feature Subset Selection）是一种用模糊扩张矩阵进行最优特征子集选择的方法。

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Based on rough sets, a new heuristic algorithm is presented to solve the difficulty. Research on the Feature Subset Selection of Complex Decision Tables

基于粗集理论，本文提出了一种新的决策表最优特征子集选择的启发式算法.复杂决策表的特征提取方法研究

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BPSO-SVM Wrapper for Feature Subset Selection

基于微粒群算法和支持向量机的特征子集选择方法

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In view of the deficiencies in traditional combination optimization method, an algorithm of feature subset selection based on genetic algorithm is proposed in this paper.

该文针对传统优化方法用于特征选择的种种缺陷，提出了基于遗传算法的特征子集选择测算法。

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Feature Subset Selection for KDD

KDD中的特征选择

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To discern positive and negative example fully, feature subset selection plays a great role in learning from examples.

特征选择是示例学习的关键，直接关系到获取的概念的优劣。

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Optimal Feature Subset Selection for Multi-class Issue

多类问题中最优特征子集选取的研究

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The heuristic algorithm of minimal feature subset selection is proved very efficient by theoretical analysis and experiment.

通过理论分析和实验，证明了该特征选取方法的高效性。

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A New Entropy-Based Feature Subset Selection Algorithm

一种新的基于信息熵的属性选择算法

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Research on a noise-tolerant algorithm for feature subset selection

容忍噪音的特征子集选择算法研究

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Optimal Feature Subset Selection of Decision Tables

决策表最优特征子集的选择&基于粗集理论的启发式算法

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Integer-programming model for learning from examples and feature subset selection based on extension matrix

示例学习与特征选择的规划模型方法

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Optimal Feature Subset Selection Using Genetic Algorithms

最优特征子集的遗传算法求解

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Incremental Feature Subset Selection Algorithm Based on Extension Matrices

基于扩张矩阵的渐进式特征子集选择算法

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An algorithm for the optimal feature subset selection

一种最优特征集的选择算法

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Feature subset selection based on max mutual information and max correlation entropy

基于最大互信息最大相关熵的特征选择方法

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