遗传程序设计
人工智能
特征向量
计算机科学
分类器(UML)
适应度函数
熵(时间箭头)
机器学习
模式识别(心理学)
水准点(测量)
数据挖掘
遗传算法
大地测量学
量子力学
物理
地理
作者
Kourosh Neshatian,Mengjie Zhang,Peter Andreae
标识
DOI:10.1109/tevc.2011.2166158
摘要
Feature construction is an effort to transform the input space of classification problems in order to improve the classification performance. Feature construction is particularly important for classifier inducers that cannot transform their input space intrinsically. This paper proposes GPMFC, a multiple-feature construction system for classification problems using genetic programming (GP). This paper takes a nonwrapper approach by introducing a filter-based measure of goodness for constructed features. The constructed, high-level features are functions of original input features. These functions are evolved by GP using an entropy-based fitness function that maximizes the purity of class intervals. A decomposable objective function is proposed so that the system is able to construct multiple high-level features for each problem. The constructed features are used to transform the original input space to a new space with better separability. Extensive experiments are conducted on a number of benchmark problems and symbolic learning classifiers. The results show that, in most cases, the new approach is highly effective in increasing the classification performance in rule-based and decision tree classifiers. The constructed features help improve the learning performance of symbolic learners. The constructed features, however, may lack intelligibility.
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