Constructing classification features using minimal predictive patterns

Iyad Batal, M. Hauskrecht
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引用次数: 22

Abstract

Choosing good features to represent objects can be crucial to the success of supervised machine learning methods. Recently, there has been a great interest in applying data mining techniques to construct new classification features. The rationale behind this approach is that patterns (feature-value combinations) could capture more underlying semantics than single features. Hence the inclusion of some patterns can improve the classification performance. Currently, most methods adopt a two-phases approach by generating all frequent patterns in the first phase and selecting the discriminative patterns in the second phase. However, this approach has limited success because it is usually very difficult to correctly identify important predictive patterns in a large set of highly correlated frequent patterns. In this paper, we introduce the minimal predictive patterns framework to directly mine a compact set of highly predictive patterns. The idea is to integrate pattern mining and feature selection in order to filter out non-informative and redundant patterns while being generated. We propose some pruning techniques to speed up the mining process. Our extensive experimental evaluation on many datasets demonstrates the advantage of our method by outperforming many well known classifiers.
使用最小的预测模式构建分类特征
选择好的特征来表示对象对于监督式机器学习方法的成功至关重要。近年来,人们对应用数据挖掘技术构建新的分类特征产生了浓厚的兴趣。这种方法背后的基本原理是模式(特征值组合)可以捕获比单个特征更多的底层语义。因此,加入一些模式可以提高分类性能。目前,大多数方法采用两阶段方法,在第一阶段生成所有频繁模式,在第二阶段选择判别模式。然而,这种方法的成功有限,因为通常很难在大量高度相关的频繁模式中正确识别重要的预测模式。在本文中,我们引入最小预测模式框架来直接挖掘一组紧凑的高预测模式。其思想是将模式挖掘和特征选择相结合,以便在生成时过滤掉无信息和冗余的模式。我们提出了一些修剪技术来加快挖掘过程。我们在许多数据集上进行了广泛的实验评估,证明了我们的方法优于许多知名分类器的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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