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Nearest neighbor based pap smear cell classification using tabu search for feature selection

Marinakis Ioannis, Dounias, G

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URI: http://purl.tuc.gr/dl/dias/2984444B-A718-4A85-AA4B-C31C8F018C42
Year 2006
Type of Item Conference Full Paper
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Bibliographic Citation Y. Marinakis ,G. Dounias. (2006).Nearest neighbor based pap smear cell classification using tabu search for feature selection.Presented at 2nd European Symposium on Nature-inspired Smart Information Systems.[online].Available:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.467.9354&rep=rep1&type=pdf
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Summary

The problem of classification consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Selecting the right set of features for classification is one of the most important problems in designing a good classifier. In this paper, a tabu search algorithm is proposed for the solution of the feature selection problem. Tabu Search is a well known and effective metaheuristic algorithm that was first introduced for the solution of combinatorial optimization problems. This algorithm is combined with a number of nearest neighbor based classifiers. The algorithm is tested in two sets of data for Pap-Smear Cell Classification. The first one consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully by cyto-technicians and doctors. Each cell is described by 20 features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem.

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