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A niching genetic programming-based multi-objective algorithm for hybrid data classification
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DOI：10.1016/j.neucom.2013.12.048.png)
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Abstract
En 中文
This paper introduces a multi-objective algorithm based on genetic programming to extract classification rules in databases composed of hybrid data, i.e., regular (e.g. numerical, logical, and textual) and non-regular (e.g. geographical) attributes. This algorithm employs a niche technique combined with a population archive in order to identify the rules that are more suitable for classifying items amongst classes of a given data set. The algorithm is implemented in such a way that the user can choose the function set that is more adequate for a given application. This feature makes the proposed approach virtually applicable to any kind of data set classification problem. Besides, the classification problem is modeled as a multi-objective one, in which the maximization of the accuracy and the minimization of the classifier complexity are considered as the objective functions. A set of different classification problems, with considerably different data sets and domains, has been considered: wines, patients with hepatitis, incipient faults in power transformers and level of development of cities. In this last data set, some of the attributes are geographical, and they are expressed as points, lines or polygons. The effectiveness of the algorithm has been compared with three other methods, widely employed for classification: Decision Tree (C4.5), Support Vector Machine (SVM) and Radial Basis Function (RBF). Statistical comparisons have been conducted employing one-way ANOVA and Tukey's tests, in order to provide reliable comparison of the methods. The results show that the proposed algorithm achieved better classification effectiveness in all tested instances, what suggests that it is suitable for a considerable range of classification applications. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Classification rules
Spatial data mining
Genetic programming
Multi-objective algorithm
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
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