Consistency-Based Feature Selection
Consistency-Based Feature Selection
复制标题
基于一致性的特征选择
DOI：
10.1007/978-3-642-04595-0_42
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Xian
中科院分区：
文献类型：
--
作者：
Kilho Shin;Xian
特征选择，即选择与分类相关的特征，是机器学习的核心问题。不一致率是评价一致性的有效指标本文通过引入两个新的一致性度量，证明了不一致率不是唯一的一致性度量，同时，表明INTERACT有一个重要的缺陷，即它不适用于特定类型的概率分布。为了解决这个问题，我们提出了两个新的算法，这两个算法具有灵活性，可以利用任何新的措施以及不一致率。通过实验比较了三种一致性测度，证明了新算法的有效性。
Feature selection, the job to select features relevant to classification, is a central problem of machine learning. Inconsistency rate is known as an effective measure to evaluate consistency (relevance) of feature subsets, and INTERACT, a state-of-the-art feature selection algorithm, takes advantage of it. In this paper, we shows that inconsistency rate is not the unique measure of consistency by introducing two new consistency measures, and also, show that INTERACT has the important deficiency that it fails for particular types of probability distributions. To fix the deficiency, we propose two new algorithms, which have flexibility of taking advantage of any of the new measures as well as inconsistency rate. Furthermore, through experiments, we compare the three consistency measures, and prove effectiveness of the new algorithms.