A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning
Pages 450 - 458
Abstract
This paper develops a first comparative study of multi- objective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective evolutionary algorithms. Thus, we design and implement SPG3P-MI based on the Strength Pareto Evolutionary Algorithm, NSG3P-MI based on the Non-dominated Sorting Genetic Algorithm and MOGLG3P-MI based on the Multi-objective genetic local search. These approaches are tested with different MIL applications and compared to a previous single-objective grammar-guided genetic programming proposal. The results demonstrate the excellent performance of multi-objective approaches in achieving accurate models and their ability to generate comprehensive rules in the knowledgable discovery process.
References
[1]
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artifical Intelligence 89(1-2), 31-71 (1997).
[2]
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS 2002: Proceedings of Neural Information Processing System, Vancouver, Canada, pp. 561-568 (2002).
[3]
Pao, H.T., Chuang, S.C., Xu, Y.Y., Fu, H.: An EM based multiple instance learning method for image classification. Expert Systems with Applications 35(3), 1468- 1472 (2008).
[4]
Yang, C., Dong, M., Fotouhi, F.: Region based image annotation through multiple-instance learning. In: Multimedia 2005: Proceedings of the 13th Annual ACM International Conference on Multimedia, New York, USA, pp. 435-438 (2005).
[5]
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: NIPS 1997: Proceedings of Neural Information Processing System 10, Denver, Colorado, USA, pp. 570-576 (1997).
[6]
Zhou, Z.H., Zhang, M.L.: Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems 11(2), 155- 170 (2007).
[7]
Zafra, A., Ventura, S., Romero, C., Herrera-Viedma, E.: Multiple instance learning with genetic programming for web mining. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 919-927. Springer, Heidelberg (2007).
[8]
Ruffo, G.: Learning single and multiple instance decision tree for computer security applications. PhD thesis, Department of Computer Science. University of Turin, Torino, Italy (2000).
[9]
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Gloriastrasse 35 (2001).
[10]
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist nondominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849-858. Springer, Heidelberg (2000).
[11]
Jaszkiewicz, A., Kominek, P.: Genetic local search with distance preserving recombination operator for a vehicle routing problem. European Journal of Operational Research 151(2), 352-364 (2003).
[12]
Zafra, A., Ventura, S.: G3P-MI: A genetic programming algorithm for multiple instance learning. In: Information Science. Elsevier, Amsterdam (submitted).
[13]
Whigham, P.A.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, California, USA, pp. 33-41 (1995).
[14]
Shukla, P.K., Deb, K.: On finding multiple pareto-optimal solutions using classical and evolutionary generating methods. European Journal of Operational Research 181(3), 1630-1652 (2007).
[15]
Parrott, D., Xiaodong, L., Ciesielski, V.: Multi-objective techniques in genetic programming for evolving classifiers. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1141-1148 (September 2005).
[16]
Mugambi, E.M., Hunter, A.: Multi-objective genetic programming optimization of decision trees for classifying medical data. In: KES 2003: Knowledge-Based Intelligent Information and Engineering Systems, pp. 293-299 (2003).
[17]
Wiens, T.S., Dale, B.C., Boyce, M.S., Kershaw, P.G.: Three way k-fold crossvalidation of resource selection functions. Ecological Modelling 212(3-4), 244-255 (2008).
[18]
Ventura, S., Romero, C., Zafra, A., Delgado, J.A., Hervás, C.: JCLEC: A java framework for evolutionary computation soft computing. Soft Computing 12(4), 381-392 (2008).
[19]
Coello, C.A., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic and Evolutionary Computation, 2nd edn. Springer, New York (2007).
[20]
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1-30 (2006).
- A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning
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Published In
June 2009
712 pages
ISBN:9783642023187
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 22 June 2009
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