A NOVEL APPROACH FOR INTEGRATING FEATURE AND INSTANCE SELECTION

摘要：

As important Machine Learning problems, Feature and Instance Selection have faced relevant improvements in the quality of the algorithms that solve them individually. However, little work has been done to implement ways to solve them simultaneously. In this paper, we introduce an algorithm that combines solutions for both problems, using a simple adaptation of the Simulated Annealing metaheuristic. Our empirical evaluation shows that, when time constraints are present, our algorithm outperforms other similar strategies.

关键词： Machine learning Feature selection Instance selection Simulated annealing

作者: JERFFESON TEIXEIRA DE SOUZA RAFAEL AUGUSTO FERREIRA DO CARMO GUSTAVO AUGUSTO LIMA DE CAMPOS

作者单位: Natural and Intelligent Computation Laboratory (LACONI), State University of Ceara (UECE)-Computer Science, 91.501-970, Fortaleza/CE, Brazil

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 374-379

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）