Negative Selection in Classification Using DBLOSUM Matrices As Affinity Function

This paper presents a novel affinity function for the Negative Selection based algorithm in binary classification. The proposed method and its classification performance are compared to several classifiers using different datasets. One of the binary classification problems includes medical testing to determine if a patient has a particular disease or not. The DBLOSUM in Negative Selection classifier appears to be best suited to classification tasks where false negatives pose a major risk, such as in medical screening and diagnosis. It is more likely than most techniques to result in false positives, but it is as accurate, if not more accurate than most other techniques.