Kishore, JK and Patnaik, LM and Mani, V and Agrawal, VK (2000) Application of Genetic Programming for Multicategory Pattern Classification. In: IEEE Transactions on Evolutionary Computation, 4 (3). pp. 242-258.
This paper explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem for the first time. GP can discover relationships among observed data and express them mathematically. Multicategory pattern classification has been done traditionally by using the maximum likelihood classifier (MLC). GP-based techniques have an advantage over statistical methods because they are distribution free, i.e., no prior knowledge is needed about the statistical distribution of the data. GP also has the ability to automatically discover the discriminant features for a class. GP has been applied for two-category (class) pattern classification, In this paper, a methodology for GP-based n- class pattern classification is developed. The given n-class problem is modeled as n two-class problems, and a genetic programming classifier expression (GPCE) is evolved as a discriminant function for each class, The GPCE is trained to recognize samples belonging to its own class and reject samples belonging to other classes. A strength of association (SA) measure is computed for each GPCE to indicate the degree to which it can recognize samples belonging to its own class. The higher the value of SA, the better is the ability of a GPCE to recognize samples belonging to its own class and reject samples belonging to other classes. The SA measure is used for uniquely assigning a class to an input Feature vector. Heuristic rules are used to prevent a GPCE with a higher SA from swamping a GPCE with a lower SA, Experimental results are presented to demonstrate the applicability of CP for multicategory pattern classification, and the results obtained are found to be satisfactory, and are compared with those of the MLC, We also discuss the various issues that arise in our approach to GP- based classification, such as the creation of training sets, the role of incremental learning, and the choice of function set in the evolution of GPCEs, as well as conflict resolution for uniquely assigning a class.
|Item Type:||Journal Article|
|Additional Information:||©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||Evolutionary computation;genetic programming;pattern classification|
|Department/Centre:||Division of Mechanical Sciences > Aerospace Engineering (Formerly, Aeronautical Engineering)|
|Date Deposited:||25 Aug 2008|
|Last Modified:||19 Sep 2010 04:15|
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