Detection of cancer patients using an innovative method for learning at imbalanced datasets

Authors:
Hamid Parvin;Behrouz Minaei-Bidgoli;Hosein Alizadeh
Affiliations:
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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Abstract

Most of standard learning algorithms presume or at least expect that distributions governed on the different classes of dataset are balanced. Also they presume that the misclassification cost of each data point is equal without considering its class. These algorithms fail to learn at the imbalanced datasets. Cancer detection is a well-known domain in which it is very common to face imbalanced class distributions. This paper presents an algorithm which is suit to this field, in both speed and efficacy. The experimental results show that the performance of the proposed algorithm outperforms some of the best methods in the field.