%0 Conference Paper %1 1068317 %A Shafti, Leila Shila %A P&\#233;rez, Eduardo P&\#233;rez %B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation %C Washington DC, USA %D 2005 %E Beyer, Hans-Georg %E O'Reilly, Una-May %E Arnold, Dirk V. %E Banzhaf, Wolfgang %E Blum, Christian %E Bonabeau, Eric W. %E Cantu-Paz, Erick %E Dasgupta, Dipankar %E Deb, Kalyanmoy %E Foster, James A. %E de Jong, Edwin D. %E Lipson, Hod %E Llora, Xavier %E Mancoridis, Spiros %E Pelikan, Martin %E Raidl, Guenther R. %E Soule, Terence %E Tyrrell, Andy M. %E Watson, Jean-Paul %E Zitzler, Eckart %I ACM Press %K Classifier Genetics-Based Learning Learning, Machine Systems, algorithms, attribute attributes construction, constructive design, experimentation, feature genetic induction, interaction, selection, shared %P 1811--1818 %T Constructive induction and genetic algorithms for learning concepts with complex interaction %U http://doi.acm.org/10.1145/1068009.1068317 %V 2 %X Constructive Induction is the process of transforming the original representation of hard concepts with complex interaction into a representation that highlights regularities. Most Constructive Induction methods apply a greedy strategy to find interacting attributes and then construct functions over them. This approach fails when complex interaction exists among attributes and the search space has high variation. In this paper, we illustrate the importance of applying Genetic Algorithms as a global search strategy for these methods and present MFE2/GA1, while comparing it with other GA-based Constructive Induction methods. We empirically analyse our Genetic Algorithm's operators and compare MFE2/GA with greedy-based methods. We also performed experiments to evaluate the presented method when concept has attributes participating in more than one complex interaction. In experiments that are conducted, MFE2/GA successfully finds interacting attributes and constructs functions to represent interactions. Results show the advantage of using Genetic Algorithms for Constructive Induction when compared with greedy-based methods. %@ 1-59593-010-8

@inproceedings{1068317, abstract = {Constructive Induction is the process of transforming the original representation of hard concepts with complex interaction into a representation that highlights regularities. Most Constructive Induction methods apply a greedy strategy to find interacting attributes and then construct functions over them. This approach fails when complex interaction exists among attributes and the search space has high variation. In this paper, we illustrate the importance of applying Genetic Algorithms as a global search strategy for these methods and present MFE2/GA1, while comparing it with other GA-based Constructive Induction methods. We empirically analyse our Genetic Algorithm's operators and compare MFE2/GA with greedy-based methods. We also performed experiments to evaluate the presented method when concept has attributes participating in more than one complex interaction. In experiments that are conducted, MFE2/GA successfully finds interacting attributes and constructs functions to represent interactions. Results show the advantage of using Genetic Algorithms for Constructive Induction when compared with greedy-based methods.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Washington DC, USA}, author = {Shafti, Leila Shila and P\&\#233;rez, Eduardo P\&\#233;rez}, biburl = {https://www.bibsonomy.org/bibtex/276ca6387d7a99db81aeb9bd1fb28ab3f/brazovayeye}, booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation}, editor = {Beyer, Hans-Georg and O'Reilly, Una-May and Arnold, Dirk V. and Banzhaf, Wolfgang and Blum, Christian and Bonabeau, Eric W. and Cantu-Paz, Erick and Dasgupta, Dipankar and Deb, Kalyanmoy and Foster, James A. and {de Jong}, Edwin D. and Lipson, Hod and Llora, Xavier and Mancoridis, Spiros and Pelikan, Martin and Raidl, Guenther R. and Soule, Terence and Tyrrell, Andy M. and Watson, Jean-Paul and Zitzler, Eckart}, interhash = {09ee73ce8f7a6ca8c5a71c549f7832d5}, intrahash = {76ca6387d7a99db81aeb9bd1fb28ab3f}, isbn = {1-59593-010-8}, keywords = {Classifier Genetics-Based Learning Learning, Machine Systems, algorithms, attribute attributes construction, constructive design, experimentation, feature genetic induction, interaction, selection, shared}, month = {25-29 June}, notes = {GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {1811--1818}, publisher = {ACM Press}, publisher_address = {New York, NY, 10286-1405, USA}, timestamp = {2008-06-19T17:51:31.000+0200}, title = {Constructive induction and genetic algorithms for learning concepts with complex interaction}, url = {http://doi.acm.org/10.1145/1068009.1068317}, volume = 2, year = 2005 }