SVDTWDD Method for High Correct Recognition Rate Classifier With Appropriate Rejection Recognition Regions

Guowei Yang,Teng Yu,Zhangjing Yang,Minghua Wan,Shaohua Qi,Zhihui Lai,Tianming Zhan,Fanlong Zhang

doi:10.1109/access.2020.2978860

Abstract

At present, regions of the same class determined by Support Vector Machines (SVM) classifier, Support Vector Domain Description (SVDD) classifier and Deep Learning (DL) classifier may occupy regions of other classes or unknown classes in feature space. There exists a risk that samples of other classes or unknown classes are wrongly classified as a known class. In this paper, the Support Vector Domain Tightly Wrapping Description Design (SVDTWDD) method with appropriate rejection regions and the corresponding incremental learning algorithm are proposed to overcome the above problem. The main work includes: (1) We develop a construction algorithm of the tightly wrapping set for the homogeneous feature set; (2) Based on the homogeneous feature set and tightly wrapping set, a novel algorithm is presented for obtaining the tightly wrapping surface of the homogeneous feature region; (3) The method for constructing all the public regions outside of the tightly wrapping surface and the intersections of wrapping regions in two different tightly wrapping surfaces, as the rejection region of the classifier; (4) An incremental algorithm is also presented based on the SVD-TWDD method. The experimental results with UCI data sets show that the correct recognition rate of our proposed method is nearly100% even if with a low rejection rate.

Highlights

For easy understanding, we first introduce several concepts
There are 4 cases for a classifier: (1) Cω = cCω, i.e., the feature area of homogeneous samples determined by the classifier equals with the feature area of actual homogeneous samples
EXPERIMENTS The effectiveness of the proposed algorithm is evaluated on UCI data set, in which our proposed method is compared to the classic Support Vector Machines (SVM), support vector data description Support Vector Domain Description (SVDD), small sphere large margin SVM (SSLM-SVM)

Summary

Introduction

In the public test data sets, the ratio of the number of rejected recognition samples to the total number of samples in the test data sets is called the rejection recognition rate of a classifier. In the test data sets without the rejected samples, the ratio of the number of correctly classified samples to the total number of samples is called the correct recognition rate with a certain rejection recognition region (set). The associate editor coordinating the review of this manuscript and approving it for publication was Wenming Cao. samples of a class ω is called the feature area (set) of homogeneous samples or the feature area (set) of actual homogeneous samples. There are 4 cases for a classifier: (1) Cω = cCω, i.e., the feature area (set) of homogeneous samples determined by the classifier equals with the feature area (set) of actual homogeneous samples

Objectives

Methods

Conclusion