feature subset selection

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。

An algorithm of feature subset selection based on genetic algorithm

基于遗传算法的特征子集选择方法

Research on the Feature Subset Selection of Complex Decision Tables

复杂决策表的特征提取方法研究

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。

Feature Subset Selection of Information System Based on Similar Extension Matrix

基于类扩张矩阵的信息系统特征选取

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

A New Entropy-Based Feature Subset Selection Algorithm

一种新的基于信息熵的属性选择算法

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。

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

Fault feature subset selection is the analysis and treatment of the information which comes from the pretreatment dynamic signal, then select the data related to the system status, and handle and diagnose the data next to extract the sensitive parameters related to the system status closely.

故障特征提取就是对系统的动态信号预处理后得到的信息进行分析和处理，提取与系统状态有关的数据，再对得到的数据进行处理和分析，提取其中与系统状态相关性较大的敏感特征。

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

Research on a Heuristic Algorithm of Feature Subset Selection Based on Entropy

基于信息熵的特征子集选择启发式算法的研究

Optimal fuzzy-valued feature subset selection ( OFFSS) method has been efficient for feature subset selection of two-class problem, which can be classified into positive and negative classes.

最优模糊值特征子集选取OFFSS（Optimal Fuzzy-Valued Feature Subset Selection）适用于数据库中示例只分为正反两类的特征子集选取问题，而不适用于多类的特征子集选取问题。

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

Optimal fuzzy-valued feature subset selection ( OFFSS) method is proposed to deal with the problem of the expression of the imprecise data. OFFSS use the fuzzy set to express the imprecise data substitute the traditional discrete method.

随着基于模糊值最优特征子集选取OFFSS的提出，对于实际应用中语义含义不精确数据（如：大，中，小）的表示，OFFSS方法采用模糊集合的方式代替了传统离散值的方式。

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。

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。

Feature Subset Selection for KDD

KDD中的特征选择

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。

OFFSS ( Optimal Fuzzy-valued Feature Subset Selection) is a new fuzzy-valued feature selection method that selects an optimal feature subset from the feature space by considering both the overall overlapping degree between two classes of examples.

OFFSS（Optimal Fuzzy-Valued Feature Subset Selection）是一种新的模糊值特征选取的方法，是基于两类事例集合的重叠程度来选取特征空间中最优特征子集。

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

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

Optimal Feature Subset Selection for Multi-class Issue

多类问题中最优特征子集选取的研究

Study on Similarity Measures for Feature Subset Selection

特征子集选取中相似性度量公式的研究

Optimal Feature Subset Selection Using Genetic Algorithms

最优特征子集的遗传算法求解

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。

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

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。

An algorithm for the optimal feature subset selection

一种最优特征集的选择算法

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.

针对此问题，提出使用基于绕封模型的故障特征选择方法，它采用遗传算法对特征集寻优，样本划分法进行错误率预测估计和BP神经网络学习算法进行分类。