特征选择:一种多目标随机优化方法

S. Rovetta, Grażyna Suchacka, Alberto Cabri, F. Masulli
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引用次数: 1

摘要

特征子集任务可以看作是一个多目标离散优化问题。在这项工作中,我们研究了一种特征子集选择方法的搜索算法组件。我们提出了一种基于阈值接受方法的算法,通过适当定义接受规则,将其扩展到多目标框架。该方法用于识别Web机器人识别问题中的相关特征子集,其中通过分析对Web服务器的HTTP请求流来识别Web上的自动软件代理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature selection: a multi-objective stochastic optimization approach
The feature subset task can be cast as a multiobjective discrete optimization problem. In this work, we study the search algorithm component of a feature subset selection method. We propose an algorithm based on the threshold accepting method, extended to the multi-objective framework by an appropriate definition of the acceptance rule. The method is used in the task of identifying relevant subsets of features in a Web bot recognition problem, where automated software agents on the Web are identified by analyzing the stream of HTTP requests to a Web server.
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