{"type":"ScholarlyArticle","title":"Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification","authors":[{"name":"Jerzy W. Bala","slug":"jerzy-w-bala","given":"Jerzy W.","family":"Bala","affiliation":"School of Information Technology and Engineering George Mason University Fairfax , VA 22030"},{"name":"Jeffrey Huang","slug":"jeffrey-huang","given":"Jeffrey","family":"Huang","affiliation":"School of Information Technology and Engineering George Mason University Fairfax , VA 22030"},{"name":"Haleh Vafaie","slug":"haleh-vafaie","given":"Haleh","family":"Vafaie","affiliation":"School of Information Technology and Engineering George Mason University Fairfax , VA 22030"},{"name":"Kenneth A. De Jong","slug":"kenneth-a-de-jong","given":"Kenneth A.","family":"De Jong","affiliation":""},{"name":"Harry Wechsler","slug":"harry-wechsler","given":"Harry","family":"Wechsler","affiliation":""}],"abstract":"This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classification. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. For a given feature subset, ID3 is invoked to produce a decision tree. The classification performance of the decision tree on unseen data is used as a measure of fitness for the given feature set, which, in turn, is used by the GA to evolve better feature sets. This GA-ID3 process iterates until a feature subset is found with satisfactory classification performance. Experimental results are presented which illustrate the feasibility of our approach on difficult problems involving recognizing visual concepts in satellite and facial image data. The results also show improved classification performance and reduced description complexity when compared against standard methods for feature selection.","doi":null,"url":"https://ijcai.org/proceedings/1995/94","pdf":"https://ijcai.org/proceedings/1995/0094.pdf","markdown":"https://ijcai.org/proceedings/1995/94.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/94","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. Mellish","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Genetic Algorithms","track":"Genetic Algorithms","number":94,"pages":{"first":719,"last":723},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Bala et al, 1996] J. Bala, P. Pachowicz, and K. De Jong, Multistrategy Learning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms, in Machine Learning: A Multistrategy Approach, Vol. IV, R.S. Michalski and G. Tecuci (Eds.), Morgan Kaufmann, San Mateo, CA., pp. 121-138, 1994.","title":"Multistrategy Learning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms","authors":"Bala","year":"1994","venue":"Machine Learning: A Multistrategy Approach","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Gruau and Whitley, 1993] F. Gruau and D. Whitley, Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect, Evolutionary Computation, Vol.l, No.3, pp. 213-234, 1993.","title":"Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect","authors":"F Gruau, D Whitley","year":"1993","venue":"Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Grefenstette et al, 1991] J. Grefenstette, L. David and D. Cerys, Genesis and OOGA : Two Genetic Algorithms System, TSP: Melorse, MA, 1991.","title":"Genesis and OOGA : Two Genetic Algorithms System, TSP: Melorse","authors":"Grefenstette","year":"1991","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hinton and Nowlan, 1987] G. E Hinton and S. J. Nolan, How Learning Can Guide Evolution, Complex Systems, Vol.l, pp. 495-502, 1987.","title":"How Learning Can Guide Evolution","authors":"G E Hinton, S J Nolan","year":"1987","venue":"Complex Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Linsker, 1988] R. Linsker, Self-Organization in a Perceptual Network, Computer, Vol. 21, No.3, pp. 105-117, 1988. 724 GENETIC ALGORITHMS [Michalski, 1994]","title":"Self-Organization in a Perceptual Network","authors":"R Linsker","year":"1988","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"R. Michalski, Inferential Theory of Learning: Developing Foundations for Multistrategy Learning, in Machine Learning: A Multistrategy Approach, Vol. IV, R.S. Michalski and G. Tecuci (Eds.), Morgan Kaufmann, San Mateo, CA., pp. 3-61, 1994.","title":"Inferential Theory of Learning: Developing Foundations for Multistrategy Learning","authors":"R Michalski","year":"1994","venue":"Machine Learning: A Multistrategy Approach","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Muhlenbein and Kinderman, 1989] H. Muhlenbein, and J. Kinderman, The dynamics of Evolution and Learning. Toward Genetic Neural Networks, in R. Pfeifer, Z. Schreter, F. Fogelman-Soulie, and L. Steels, Connectionism in Perspective, Elsevier Science, pp. 173-197, 1989.","title":"The dynamics of Evolution and Learning. Toward Genetic Neural Networks","authors":"H Muhlenbein, J Kinderman","year":"1989","venue":"Connectionism in Perspective","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Quinlan, 1986] J. R. Quinlan, The Effect of Noise on Concept Learning, in Machine Learning: an Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell am T.M. Mitchell (Eds.), Morgan Kaufmann publishers, San Mateo, CA, pp. 149-166, 1986.","title":"The Effect of Noise on Concept Learning","authors":"J R Quinlan","year":"1986","venue":"Machine Learning: an Artificial Intelligence Approach","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Vafaie and De Jong, 1994] H. Vafaie and K. De Jong, Improving a Rule Induction System Using Genetic Algorithms, in Machine Learning: A Multistrategy Approach, Vol. IV, R.S. Michalski and G. Tecuci (Eds.), Morgan Kaufmann, San Mateo, CA., pp. 453-469, 1994.","title":"Improving a Rule Induction System Using Genetic Algorithms","authors":"H Vafaie, K De Jong","year":"1994","venue":"Machine Learning: A Multistrategy Approach","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Wechsler, 1993] H. Wechsler, A Perspective on Evolution and the Lamarckian Hypothesis Using artificial Worlds arJ Genetic Algorithms, Revue Internationale de Systernique, Vol. 7, No. 5, pp. 573-592, 1993.","title":"A Perspective on Evolution and the Lamarckian Hypothesis Using artificial Worlds arJ Genetic Algorithms","authors":"H Wechsler","year":"1993","venue":"Revue Internationale de Systernique","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Whitley et al, 1994] D. Whitley, V. S. Gordon, and K. Mathias, Lamarckian Evolution, the Baldwin Effect, arJ Function Optimization, in Y. Davidor, H.P. Schwefel, and R. Manner (Eds.), Parallel Problem Solving from Nature-PPSN III, Springer Verlag, pp. 6-15, 1994.","title":"Lamarckian Evolution, the Baldwin Effect, arJ Function Optimization","authors":"D Whitley, V S Gordon, K Mathias","year":"1994","venue":"Parallel Problem Solving from Nature-PPSN III","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}