Instance-Based Classification Through Hypothesis Testing
Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9333560/ |
| _version_ | 1818856964242800640 |
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| author | Zengyou He Chaohua Sheng Yan Liu Quan Zou |
| author_facet | Zengyou He Chaohua Sheng Yan Liu Quan Zou |
| author_sort | Zengyou He |
| collection | DOAJ |
| description | Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address the issue of statistical significance. In this paper, we formulate the binary classification problem as a two-sample testing problem. More precisely, our classification model is a generic framework that is composed of two steps. In the first step, the distance between the test instance and each training instance is calculated to derive two distance sets. In the second step, the two-sample test is performed under the null hypothesis that the two sets of distances are drawn from the same cumulative distribution. After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value. Essentially, the presented classification method can be regarded as an instance-based classifier based on hypothesis testing. The experimental results on 38 real data sets show that our method is able to achieve the same level performance as several classic classifiers and has significantly better performance than existing testing-based classifiers. Furthermore, we can handle outlying instances and control the false discovery rate of test instances assigned to each class under the same framework. |
| first_indexed | 2024-12-19T08:32:51Z |
| format | Article |
| id | doaj.art-9719b6314f43425daf8f4f682699fdb0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:32:51Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9719b6314f43425daf8f4f682699fdb02022-12-21T20:29:07ZengIEEEIEEE Access2169-35362021-01-019174851749410.1109/ACCESS.2021.30537789333560Instance-Based Classification Through Hypothesis TestingZengyou He0https://orcid.org/0000-0001-9526-8816Chaohua Sheng1https://orcid.org/0000-0002-6392-2411Yan Liu2https://orcid.org/0000-0002-1386-812XQuan Zou3https://orcid.org/0000-0001-6406-1142Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaSchool of Software, Dalian University of Technology, Dalian, ChinaInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, ChinaClassification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address the issue of statistical significance. In this paper, we formulate the binary classification problem as a two-sample testing problem. More precisely, our classification model is a generic framework that is composed of two steps. In the first step, the distance between the test instance and each training instance is calculated to derive two distance sets. In the second step, the two-sample test is performed under the null hypothesis that the two sets of distances are drawn from the same cumulative distribution. After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value. Essentially, the presented classification method can be regarded as an instance-based classifier based on hypothesis testing. The experimental results on 38 real data sets show that our method is able to achieve the same level performance as several classic classifiers and has significantly better performance than existing testing-based classifiers. Furthermore, we can handle outlying instances and control the false discovery rate of test instances assigned to each class under the same framework.https://ieeexplore.ieee.org/document/9333560/Classificationhypothesis testingtwo-sample testingmachine learning |
| spellingShingle | Zengyou He Chaohua Sheng Yan Liu Quan Zou Instance-Based Classification Through Hypothesis Testing IEEE Access Classification hypothesis testing two-sample testing machine learning |
| title | Instance-Based Classification Through Hypothesis Testing |
| title_full | Instance-Based Classification Through Hypothesis Testing |
| title_fullStr | Instance-Based Classification Through Hypothesis Testing |
| title_full_unstemmed | Instance-Based Classification Through Hypothesis Testing |
| title_short | Instance-Based Classification Through Hypothesis Testing |
| title_sort | instance based classification through hypothesis testing |
| topic | Classification hypothesis testing two-sample testing machine learning |
| url | https://ieeexplore.ieee.org/document/9333560/ |
| work_keys_str_mv | AT zengyouhe instancebasedclassificationthroughhypothesistesting AT chaohuasheng instancebasedclassificationthroughhypothesistesting AT yanliu instancebasedclassificationthroughhypothesistesting AT quanzou instancebasedclassificationthroughhypothesistesting |