Comparison of three classification techniques, CART, C4.5 and multi-layer perceptrons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Neural Computation
Bayesian classification with correlation and inheritance
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Two-fold spatiotemporal regression modeling in wireless sensor networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Expert Systems with Applications: An International Journal
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This paper presents a new data classification method based on particle swarm optimization (PSO) techniques. The paper discusses the building of a classifier model based on multiple regression linear approach. The coefficients of multiple regression linear models (MRLMs) are estimated using least square estimation technique and PSO techniques for percentage of correct classification performance comparisons. The mathematical models are developed for many real world datasets collected from UCI machine repository. The mathematical models give the user an insight into how the attributes are interrelated to predict the class membership. The proposed approach is illustrated on many real data sets for classification purposes. The comparison results on the illustrative examples show that the PSO based approach is superior to traditional least square approach in classifying multi-class data sets.