Mining multiple comprehensible classification rules using genetic programming
Kay Chen Tan,Arthur Tay,Tong Heng Lee,C. M. Heng +3 more
- 12 May 2002
- Vol. 2, pp 1302-1307
TL;DR: The tree representation of GP is extended to evolve multiple comprehensible IF-THEN classification rules and a concept mapping technique for the fitness evaluation of individuals is introduced.
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Abstract: Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.
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Citations
A Survey on the Application of Genetic Programming to Classification
P.G. Espejo,Sebastián Ventura,Francisco Herrera +2 more
- 01 Mar 2010
TL;DR: This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.
600
Using Data Mining for Due Date Assignment in a Dynamic Job Shop Environment
D. Y. Sha,C.-H. Liu +1 more
TL;DR: In this article, a rule-based total work content (TWK) due date assignment (RTWK) model is proposed to improve the performance of the TWK method.
Artificial intelligence approach to classify unipolar and bipolar depressive disorders
TL;DR: A study using two-step hybridized methodology: particle swarm optimization (PSO) algorithm for FS process and ANN for training process stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected features from alpha and theta frequency bands with 89.89 % overall classification accuracy.
59
Speeding up the evaluation phase of GP classification algorithms on GPUs
Alberto Cano,Amelia Zafra,Sebastián Ventura +2 more
- 01 Feb 2012
TL;DR: In this article, the authors proposed an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time.
A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems
Vijay Ingalalli,Sara Silva,Mauro Castelli,Leonardo Vanneschi +3 more
- 23 Apr 2014
TL;DR: A novel algorithm for tree based GP is presented, that incorporates some ideas on the representation of the solution space in higher dimensions and lays some foundations on addressing multi-class classification problems using GP, which may lead to further research in this direction.
50
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