Induction of decision trees as classification models through metaheuristics br

The induction of decision trees is a widely-usedapproachtobuildclassificationmodelsthatguaranteehighperfor-manceandexpressiveness.Sincearecursive-partitioningstrategyguidedforsomesplittingcriterioniscommonlyusedtoinducetheseclassifiers,overfitting,attributeselectionbias,andinstabilitytosmalltrainingsetchangesarewell-knownproblemsinthem.Otherapproaches,suchasincrementalinduction,classifierensembles,andtheglobalsearchinthedecision-tree-space,havebeenimplementedtoovercometheseproblems.Inparticular,metaheuristicssuchassimulatedannealing,geneticalgorithms,geneticprogramming,andantcolonyoptimiza-tionhavebeenusedtoinducecompactandaccuratedecisiontrees.Thispaperpresentsastate-of-the-artreviewoftheuseofsingle-solution-basedmetaheuristicsandswarmandevolutionarycomputationalgorithmstobuilddecisiontreesasclassificationmodels.Weoutlinethedecision-tree-inductionprocesscomponentsanddetailtheexistingliteraturestudiesonmetaheuristic-basedapproachestobuildingtheseclassifiers.Severaltimelinesshow-ingthechronologicalorderinwhichtheseapproacheswereintroducedintheliteratureareincluded.Asummaryanalysisofthesestudiesisalsoconducted,focusingontheirinternalcomponentsandexperimentalstudies.Thisworkprovidesausefulreferencepoint for future research in this field

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This paper presents a state-of-the-art review of using single-solution-based metaheuristics and swarm and evolutionary computational algorithms to build decision trees as classification models, outlining the decision-tree induction process components and detailing existing literature studies on metaheuristic-based approaches to building these classifiers. A summary analysis of these studies is also conducted, focusing on their internal components and experimental studies.