Cost-sensitive learning using logical analysis of data

Classification is a common task in data mining that assigns a class label to an unseen situation. It has been widely used in decision making for various applications, and many machine learning algorithms have been developed to accomplish this task. Classification becomes critical when the problem under concern is related to serious situations such as fraud detection, cancer diseases, and quality control. Learning in these situations is characterized by predetermined asymmetric costs of incorrect class prediction, or critical consequences associated with erroneous class prediction. In this paper, a novel approach of cost-sensitive learning is proposed. The approach is constructed by employing the theory of logical analysis of data (LAD) to build accurate cost-sensitive classifiers. Two classifiers are proposed. The first classifier is established by solving a proposed pattern selection model, minimum misclassification cost model (MMCM), that aims at minimizing the asymmetric misclassification cost. The second classifier is established by solving another proposed pattern selection model, maximum precision-recall model (MPRM), that maximizes precision and recall willing to reach a 100% accuracy. A comparative study is conducted by using real datasets. The proposed MMCM has enabled LAD to realize up to 32.22% cost reduction from the misclassification cost realized by the traditional implementation of LAD. Moreover, MPRM has provided up to 19.15% increase in the precision and up to 37% increase in the recall. Also, MPRM has enhanced the performance of LAD while compared to common machine learning algorithms by providing better combinations of recall and false positive rate. This enabled LAD to provide the closet to the optimal point on the receiver operating characteristic (ROC) diagram when compared with existing machine learning methods. Incorporating the MMCM and the MPRM models into LAD establishes a novel implementation of LAD that makes LAD a promising cost-sensitive learning classifier compared to other machine learning classifiers.