Improving Performance of Nearest Neighborhood Classifier Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Majid:2004:ICMLA,

• author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza",
• title = "Improving Performance of Nearest Neighborhood Classifier Using Genetic Programming",
• booktitle = "The Third International Conference on Machine Learning and Applications (ICMLA-04)",
• year = "2004",
• pages = "469--476",
• address = "Louisville, KY, USA",
• month = "16-18 " # dec,
• organisation = "IEEE/ACM",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICMLA.2004.1383552",
• size = "8 pages",
• abstract = "Nearest neighbourhood classifier (kNN) is most widely used in pattern recognition applications. Depending on the selection of voting methodology, the problem of outliers has been encountered in this classifier. Therefore, selection and optimisation of the voting methodology is very important. In this work, we have used Genetic Programming (GP) to improve the performance of nearest neighbour classifier. Instead of using predefined k nearest neighbors, the number of men and women in the first two quartiles in Euclidean space are used for voting. GP is, then, used to evolve an optimal class mapping function that effectively reduces the outliers. The performance of modified nearest neighborhood (ModNN) classifier is then compared with the conventional kNN for gender classification problem. Receiver Operating Characteristics curve and its Area Under the Convex Hull (A UCH) are used as the performance measures. Considering the first three and first five eigen features respectively, ModNN achieves AUCH equal to 0.985 and 0.992 as compared to 0.9693 and 0.9795 of conventional kNN respectively",
• notes = "Broken Jan 2013 http://www.cs.csubak.edu/~icmla/icmla04/ also known as \cite{1383552}",

}

Genetic Programming entries for Abdul Majid Asifullah Khan Anwar M Mirza

Citations