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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.

A Belief Theory-Based Decision System for Breast Cancer Data