Evolutionary and metaheuristics based data mining
DM poses a great range of interesting (NP-hard) problems that consists mainly in searching for: the pattern/model that best describe the data, the more predictive subset of variables, the more accurate parameter configuration, etc. Because of this, and of the success obtained by metaheuristics when...
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| Published in: | Soft computing (Berlin, Germany) Vol. 13; no. 3; pp. 209 - 212 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.02.2009
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | DM poses a great range of interesting (NP-hard) problems that consists mainly in searching for: the pattern/model that best describe the data, the more predictive subset of variables, the more accurate parameter configuration, etc. Because of this, and of the success obtained by metaheuristics when applied to other combinatorial/numerical optimization problems, metaheuristics have been widely applied to solve DM problems during the last years. [...]the use of evolutionary algorithms and metaheuristics in general to approach DM-based problems (EMBDM) is a hot topic of research nowadays. [...]there is a very active related field of research known as learning classifier systems (LCS) (Bull et al. 2002) that learns the rule-based classifier by combining evolutionary algorithms with reinforcement learning. In descriptive data mining there is no class variable so we are in an unsupervised setting. [...]the goal now is not to predict a target value, but to discover intrinsic relations, dependences, etc. of the data. On the contrary of Bayesian networks that represent a global (in)dependence model among all the variables, association rules can be viewed as a set of separate dependence sentences, where each rule stands for the dependence relation between the variables in the antecedent and the one in the consequent. Because different measures (e.g. support, confidence, interestingness) are used to evaluate a the goodness of a rule, the multiobjective approach has shown to be a good tool to deal with it (Ghosh and Nath 2004). |
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| AbstractList | DM poses a great range of interesting (NP-hard) problems that consists mainly in searching for: the pattern/model that best describe the data, the more predictive subset of variables, the more accurate parameter configuration, etc. Because of this, and of the success obtained by metaheuristics when applied to other combinatorial/numerical optimization problems, metaheuristics have been widely applied to solve DM problems during the last years. [...]the use of evolutionary algorithms and metaheuristics in general to approach DM-based problems (EMBDM) is a hot topic of research nowadays. [...]there is a very active related field of research known as learning classifier systems (LCS) (Bull et al. 2002) that learns the rule-based classifier by combining evolutionary algorithms with reinforcement learning. In descriptive data mining there is no class variable so we are in an unsupervised setting. [...]the goal now is not to predict a target value, but to discover intrinsic relations, dependences, etc. of the data. On the contrary of Bayesian networks that represent a global (in)dependence model among all the variables, association rules can be viewed as a set of separate dependence sentences, where each rule stands for the dependence relation between the variables in the antecedent and the one in the consequent. Because different measures (e.g. support, confidence, interestingness) are used to evaluate a the goodness of a rule, the multiobjective approach has shown to be a good tool to deal with it (Ghosh and Nath 2004). |
| Author | Puerta, José M. del Jesús, María J. Gámez, José A. |
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| Cites_doi | 10.1023/A:1007677805582 10.1016/j.neucom.2005.12.014 10.1109/3477.907563 10.1109/TEVC.2002.806857 10.1109/TEVC.2005.859371 10.1109/TPAMI.2004.105 10.1016/S0167-8655(99)00057-4 10.1109/TFUZZ.2007.900904 10.1007/s10710-005-2988-7 10.1016/S0888-613X(02)00091-9 10.1142/S0218488507004868 10.1016/j.ins.2003.03.021 10.1016/j.ejor.2004.08.012 10.1007/s00500-006-0128-9 10.1109/TSMCB.2002.805696 10.1016/j.patcog.2008.02.006 10.1142/4177 10.1007/s005000100110 10.3233/IDA-2007-11506 10.1007/978-1-4615-1539-5 |
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| SubjectTerms | Artificial Intelligence Bayesian analysis Classification Classifiers Clustering Combinatorial analysis Computational Intelligence Control Data mining Dependence Editorial Engineering Evolutionary algorithms Fuzzy sets Genetic algorithms Heuristic methods Machine learning Mathematical Logic and Foundations Mechatronics Neural networks Optimization Robotics Variables |
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