Instance-based entropy fuzzy support vector machine for imbalanced data

Abstract

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample's neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.

Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision

“…As previously mentioned, we employ IEFSVM in [26], an advanced model of EFSVM, for loan prediction in P2P lending market. Many literatures have proposed methods to compute the fuzzy membership of FSVM; Fan et al [12] suggested to utilize the EFSVM by incorporating the nearest neighbors entropy for imbalanced dataset.…”

“…However, the disadvantage of this method lies on a unified k for all instances. In this paper, we suggest to utilize IEFSVM proposed in [26] to reflect the change of entropy, which varies in response to different k, in determination of fuzzy membership. The determination of such fuzzy membership is based on the following diversity pattern of nearest neighbors entropy.…”

“…The above four logics suggest that s i should decrease when d i and θ i increase. Based on this idea, the instance-based entropy fuzzy membership incorporating the nearest neighbors entropy information can be defined as follows [26].…”

“…Instead of tuning neighborhood size as a fixed value, it is reasonable to combine information in response to various neighborhood size to assign fuzzy membership of each data point. In this regard, we suggest to apply an instance-based entropy fuzzy support vector machine (IEFSVM), proposed in [26], in loan evaluation for P2P lending.…”

A kernel-free fuzzy support vector machine with Universum

“…Gupta et al [10] further proposed a fuzzy twin SVM based on information entropy. Cho et al [6] modified EFSVM and introduced instance-based entropy fuzzy SVM. By combining entropy information of Universum data, a fuzzy Universum SVM (FUSVM) and a fuzzy Universum twin SVM are proposed in [23].…”