A Belief Theory-Based Decision System for Breast Cancer Data

Abstract

Belief theory involving the use of Shafer's model and source combination rules has found wide use for the development and improvement of classification models. Availability of theoretical constructs for making the best use of available information including the ability to deal with ignorance makes it an attractive option. Representation of ignorance means that we can deal with hard to classify samples by deferring the decision rather than taking an erroneous one. This makes it especially useful in the biomedical field where the cost of making an error is high. In this paper, we develop a belief theory-based classification model and apply it to the Wisconsin breast cancer database. The proposed method is compared with an existing belief theory-based classifier, the conventional support vector machine classifier and other recent approaches using different performance parameters such as error rate, false-positive rate, false negative rate, and confusion matrix. The proposed model is found to outperform the approaches under consideration and is also able to deal with data samples having missing feature values effectively.

KEYWORDS:

• Belief theory
• Dempster–Shafer theory
• Wisconsin breast cancer data
• Classification
• Disjunctive rule
• Dempster rule

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Shameer Faziludeen

Shameer Faziludeen is currently a research scholar in the Department of Electronics & Communication, National Institute of Technology, Calicut, India. He obtained his Bachelor of Technology in applied electronics and instrumentation engineering from University of Kerala in 2009. He received his Master of Technology degree from University of Calicut in 2013. His research interests are in machine learning with emphasis on application of belief theory to classifiers.

Praveen Sankaran

Praveen Sankaran is currently an assistant professor in the Department of Electronics & Communication, National Institute of Technology, Calicut, India. Prior to joining NITC, he was an adjunct faculty in the Electrical and Computer Engineering (ECE) Department at Old Dominion University. Dr Sankaran received his Bachelor of Technology in applied electronics and instrumentation engineering from University of Calicut in 2002 and Master of Science in electrical engineering from Old Dominion University, Norfolk, Virginia in 2005. He earned his PhD in electrical and computer engineering from Old Dominion University, Norfolk, Virginia in 2009. His research interests are in nonlinear feature extraction methods for data classification, biomedical signal processing and deep learning. Email: [email protected]