Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
An Imunogenetic Technique To Detect Anomalies In Network Traffic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Speed boosting induction of fuzzy rules with artificial immune systems
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Mining fuzzy classification rules using an artificial immune system with boosting
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
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The aim of this study was to use a machine learning approach combining fuzzy modeling with an immune algorithm to model sport training, in particular swimming. A proposed algorithm mines the available data and delivers the results in a form of a set of fuzzy rules ''IF (fuzzy conditions) THEN (class)''. Fuzzy logic is a powerful method to cope with continuous data, to overcome problem of overlapping class definitions, and to improve the rule comprehensibility. Sport training is modeled at the level of microcycle and training unit by 12 independent attributes. The data was collected in two months (February-March 2008), among swimmers from swimming sections in Wroclaw, Poland. The swimmers had minimum of 7 years of training and reached the II class level in swimming classification from 2005 to 2008. The goal of the performed experiments was to find the rules answering the question - how does the training unit influence swimmer's feelings while being in water the next day? The fuzzy rules were inferred for two different scales of the class to be predicted. The effectiveness of the learned set of rules reached 68.66%. The performance, in terms of classification accuracy, of the proposed approach was compared with traditional classifier schemes. The accuracy of the result of compared methods is significantly lower than the accuracy of fuzzy rules obtained by a method presented in this study (paired t-test, P