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Real-world classification problems usually involve imbalanced data sets. In such cases, a classifier with high classification accuracy does not necessarily imply a good classification performance for all classes. The Area Under the ROC Curve (AUC) has been recognized as a more appropriate performance indicator in such cases. Quite a few methods have been developed to design classifiers with the maximum...
In binary classification problems, receiver operating characteristic (ROC) graphs are commonly used for visualizing, organizing and selecting classifiers based on their performances. An important issue in the ROC literature is to obtain the ROC convex hull (ROCCH) that covers potentially optima for a given set of classifiers [1]. Maximizing the ROCCH means to maximize the true positive rate (tpr)...
In this work, we propose a new genetic programming algorithm with local search strategies, named Memetic Genetic Programming(MGP), for classification problems. MGP aims to acquire a classifier with large Area Under the ROC Curve (AUC), which has been proved to be a better performance metric for traditionally used metrics (e.g., classification accuracy). Three new points are presented in our new algorithm...
In the field of imbalance learning and cost sensitive learning, minimization of the classification error rate is not an appropriate approach due to class skew and cost distributions. Thus the area under the ROC Curve (AUC) has been widely utilized to assess the performance of the classifiers in such cases. The Maximum AUC Linear Classifier (MALC), aiming at maximizing AUC directly, is a nonparametric...
Financial forecasting is a lucrative and complicated application of machine learning. In this paper, we focus on the finding investment opportunities. We therefore explore four different Genetic Programming approaches and compare their performances on real-world data. We find that the novelties we introduced in some of these approaches indeed improve the results. However, we also show that the Genetic...
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