A rough set approach to feature selection based on power set tree
A rough set approach to feature selection based on power set tree
复制标题
一种基于幂集树的粗集特征选择方法
DOI：
10.1016/j.knosys.2010.09.004
复制
发表时间：
2011-03-01
影响因子：
8.8
通讯作者：
Wu, Keshou
中科院分区：
文献类型：
--
作者：
Chen, Yumin;Miao, Duoqian;Wu, Keshou
特征选择是模式识别、机器学习和数据挖掘的重要预处理步骤。传统的爬山搜索特征选择方法很难找到最优约简。而目前的随机搜索策略，如GA，ACO和PSO，提供了一个更强大的解决方案，但在增加计算工作量的代价。研究快速有效的搜索算法是非常必要的。粗糙集理论提供了一种数学工具，通过纯粹的结构化方法发现数据依赖关系并减少数据集中包含的特征数量。在本文中，我们定义了一种称为幂集树（PS-树）的结构，这是一个表示幂集的序树，每个可能的约简被映射到树的一个节点。然后，我们提出了一个粗糙集方法的特征选择的基础上PS-树。给出了PS树的两种剪枝规则。并给出了两种新的基于PS树的特征选择算法。实验结果表明，我们的算法是有效的和高效的。(C)2010 Elsevier B. V.保留所有权利。
Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Traditional hill-climbing search approaches to feature selection have difficulties to find optimal reducts. And the current stochastic search strategies, such as GA, ACO and PSO, provide a more robust solution but at the expense of increased computational effort. It is necessary to investigate fast and effective search algorithms. Rough set theory provides a mathematical tool to discover data dependencies and reduce the number of features contained in a dataset by purely structural methods. In this paper, we define a structure called power set tree (PS-tree), which is an order tree representing the power set, and each possible reduct is mapped to a node of the tree. Then, we present a rough set approach to feature selection based on PS-tree. Two kinds of pruning rules for PS-tree are given. And two novel feature selection algorithms based on PS-tree are also given. Experiment results demonstrate that our algorithms are effective and efficient. (C) 2010 Elsevier B.V. All rights reserved.