南京大学学报：自然科学版2012，Vol.48Issue(4)：499-503,5.
基于局部能量的集成特征选择
Ensemble feature selection based on local energy
摘要
Abstract
Feature selection is one of the key problems in machine learning and data mining to reduce the dimensionality of data, and the stability of feature selection is one of the current hot points. Stability is the insensitivity of the result of a feature selection algorithm to variations of the training set. This issue is particularly critical for applications where feature selection is used as a knowledge discovery tool for identifying characteristic markers to explain the observed phenomena. In the paper, on the one hand, a feature selection algorithm-Lmba is introduced in detail, and the evaluation criterion is deeply analyzed in terms of energy-based model. Lmba can be considered as one of feature ranking algorithm based on local-energy of samples. On the other hand, in order to improve its stability, an ensemble version of local energy-based feature ranking is proposed based on the recognition that ensemble learning is very effective for stability improvement. Some experiments are conducted on real-world data sets to show the higher stability of ensemble results than the single one.关键词
特征选择/能量学习/集成Key words
feature selection/energy based model/ensemble分类
计算机与自动化引用本文复制引用
季薇,李云..基于局部能量的集成特征选择[J].南京大学学报：自然科学版,2012,48(4):499-503,5.基金项目
国家自然科学基金（61073114）,江苏省高校自然科学基金（08KJB520008,09KJBS10012）,南京邮电大学人才引进启动基金和攀登计划 （）
