Instance Selection in Logical Rule Extraction for Regression Problems

The paper presents three algorithms of instance selection for regression problems, which extend the capabilities of the CNN, ENN and CA algorithms used for classification tasks. Various combinations of the algorithms are experimentally evaluated as data preprocessing for regression tree induction. The influence of the instance selection algorithms and their parameters on the accuracy and rules produced by regression trees is evaluated and compared to the results obtained with tree pruning.

[1]  Jianping Zhang,et al.  Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[2]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing, 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part I , 2010, International Conference on Artificial Intelligence and Soft Computing.

[3]  Francisco Herrera,et al.  Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability , 2007, Data Knowl. Eng..

[4]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[5]  Jacek M. Zurada,et al.  Computational intelligence methods for rule-based data understanding , 2004, Proceedings of the IEEE.

[6]  Sung-Bae Cho,et al.  Hybrid Artificial Intelligent Systems , 2015, Lecture Notes in Computer Science.

[7]  Shuning Wu,et al.  Optimal instance selection for improved decision tree , 2007 .

[8]  Ginés Rubio,et al.  Applying Mutual Information for Prototype or Instance Selection in Regression Problems , 2009, ESANN.

[9]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[10]  Marcin Blachnik,et al.  A Hybrid System with Regression Trees in Steel-Making Process , 2011, HAIS.

[11]  Marek Grochowski,et al.  Comparison of Instances Seletion Algorithms I. Algorithms Survey , 2004, ICAISC.

[12]  Marcin Blachnik,et al.  Do We Need Whatever More Than k-NN? , 2010, ICAISC.

[13]  Francisco Herrera,et al.  Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Jörg H. Siekmann,et al.  Artificial Intelligence and Soft Computing - ICAISC 2004 , 2004, Lecture Notes in Computer Science.

[15]  Tony R. Martinez,et al.  Reduction Techniques for Instance-Based Learning Algorithms , 2000, Machine Learning.

[16]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[17]  Marcin Blachnik,et al.  Temperature Prediction in Electric Arc Furnace with Neural Network Tree , 2011, ICANN.

[18]  J. Tolvi,et al.  Genetic algorithms for outlier detection and variable selection in linear regression models , 2004, Soft Comput..

[19]  Chin-Liang Chang,et al.  Finding Prototypes For Nearest Neighbor Classifiers , 1974, IEEE Transactions on Computers.

[20]  Rm Cameron-Jones,et al.  Instance Selection by Encoding Length Heuristic with Random Mutation Hill Climbing , 1995 .

[21]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .