Title page for ETD etd-11162009-144820

Type of Document

Dissertation

Author

Lawson, Mark Jon

Author's Email Address

malawso4@vt.edu

URN

etd-11162009-144820

Title

The Search for a Cost Matrix to Solve Rare-Class Biological Problems

Degree

PhD

Department

Computer Science

Advisory Committee

Advisor Name	Title
Zhang, Liqing	Committee Chair
Fan, Weiguo Patrick	Committee Member
Heath, Lenwood S.	Committee Member
Ramakrishnan, Naren	Committee Member
Wang, G. Alan	Committee Member

Keywords

Local Search
Bioinformatics
Machine Learning
Classification

Date of Defense

2009-11-02

Availability

unrestricted

Abstract

The rare-class data classification problem is a common one. It occurs when, in a dataset, the
class of interest is far outweighed by other classes, thus making it difficult to classify using
typical classification algorithms. These types of problems are found quite often in biological
datasets, where data can be sparse and the class of interest has few representatives. A variety
of solutions to this problem exist with varying degrees of success.
In this paper, we present our solution to the rare-class problem. This solution uses MetaCost,
a cost-sensitive meta-classifier, that takes in a classification algorithm, training data, and a
cost matrix. This cost matrix adjusts the learning of the classification algorithm to classify
more of the rare-class data but is generally unknown for a given dataset and classifier. Our
method uses three different types of optimization techniques (greedy, simulated annealing,
genetic algorithm) to determine this optimal cost matrix. In this paper we will show how
this method can improve upon classification in a large amount of datasets, achieving better
results along a variety of metrics. We will show how it can improve on different classification
algorithms and do so better and more consistently than other rare-class learning techniques
like oversampling and undersampling. Overall our method is a robust and effective solution
to the rare-class problem.

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