Application of genetic programming for multicategory pattern classification
By Jandhyala Kishore, Lalit M. Patnaik, V. Mani, V. Agrawal. IEEE Transactions on Evolutionary Computation, 2000
301 citations recorded by the source corpus.
Explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem. GP can discover relationships and express them mathematically. 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 automatically discovers the discriminant features for a class. GP has been applied for two-category classification. A methodology for GP-based n-class classification is developed. The 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 others. A strength of association (SA) measure is computed for each GPCE to indicate the degree to which it can recognize samples of its own class. SA 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 one with a lower SA. Experimental results are presented to demonstrate the applicability of GP for multicategory classification, and they are found to be satisfactory. 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 GPCE, as well as conflict resolution for uniquely assigning a class.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/2c043021487112dc4b9a187a4a91d747c2dd0f50
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In Artificial Intelligence
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Citations in the corpus
- Genetic programming for simultaneous feature selection and classifier design (2006, background): “This is equivalent to copying each point in the training set of class by times [39].”
- A Survey on the Application of Genetic Programming to Classification (2010, background): “ing multiclass classification with GP-evolved discriminant functions is [131], where an n-class problem is converted into n binary problems.”