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A Probabilistic Approach to Feature Selection - A Filter Solution

机译：特征选择的概率方法-过滤器解决方案

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Feature selection can be defined as a problem of finding a minimum set of M relevant attributes that describes the dataset as well as the original N attributes do, where M ≤ N. After examining the problems with both the exhaustive and the heuristic approach to feature selection, this paper proposes a probabilistic approach. The theoretic analysis and the experimental study show that the proposed approach is simple to implement and guaranteed to find the optimal if resources permit. It is also fast in obtaining results and effective in selecting features that improve the performance of a learning algorithm. An on-site application involving huge datasets has been conducted independently. It proves the effectiveness and scalability of the proposed algorithm. Discussed also are various aspects and applications of this feature selection algorithm.

机译：特征选择可以定义为以下问题：找到描述数据集的M个相关属性的最小集合以及原始N个属性（其中M≤N）。在研究了详尽且启发式的特征选择方法之后，本文提出了一种概率方法。理论分析和实验研究表明，该方法易于实现，并且在资源允许的情况下可以保证找到最优方法。它还快速获得结果，并且有效地选择了改善学习算法性能的特征。涉及大量数据集的现场应用已独立进行。证明了该算法的有效性和可扩展性。还讨论了该特征选择算法的各个方面和应用。

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来源
《Machine learning》|1996年|319-327|共9页
会议地点 Bari(IT);Bari(IT)
作者
Huan Liu; Rudy Setiono;
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作者单位

Department of Information Systems and Computer Science. National University of Singapore Kent Ridge, Singapore 119260;

Department of Information Systems and Computer Science. National University of Singapore Kent Ridge, Singapore 119260;

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原文格式 PDF
正文语种 eng
中图分类计算机的应用;
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6. A filter-based feature selection approach for identifying potential biomarkers for lung cancer [O] . In-Hee Lee, Gerald H Lushington, Mahesh Visvanathan 2011

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7. Table 1: F1 score () obtained using term frequency based feature selection approach and multivariate filter feature selection approach (SemEval Dataset). [O] . -1

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A Probabilistic Approach to Feature Selection - A Filter Solution

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