Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group
Ensemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. A heterogeneous ensemble can generate classifiers with better diversity than a homogeneous ensemble and improve the performanc...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/1/103 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549174509666304 |
|---|---|
| author | Meng Han Shurong Yang Hongxin Wu Jian Ding |
| author_facet | Meng Han Shurong Yang Hongxin Wu Jian Ding |
| author_sort | Meng Han |
| collection | DOAJ |
| description | Ensemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. A heterogeneous ensemble can generate classifiers with better diversity than a homogeneous ensemble and improve the performance of classification results. An Adaptive Heterogeneous Classifier Group (AHCG) algorithm is proposed. The AHCG first proposes the concept of a Heterogeneous Classifier Group (HCG); that is, two groups of different ensemble classifiers are used in the testing and training phases. Secondly, the Adaptive Selection Strategy (ASS) is proposed, which can select the ensemble classifiers to be used in the test phase. The least squares method is used to calculate the weights of the base classifiers for the in-group classifiers and dynamically update the base classifiers according to the weights. A large number of experiments on seven datasets show that this algorithm has better performance than most existing ensemble classification algorithms in terms of its accuracy, example-based F1 value, micro-averaged F1 value, and macro-averaged F1 value. |
| format | Article |
| id | doaj-art-c0c76ccee10c434099ca28108e066a24 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-c0c76ccee10c434099ca28108e066a242025-01-10T13:18:16ZengMDPI AGMathematics2227-73902024-12-0113110310.3390/math13010103Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier GroupMeng Han0Shurong Yang1Hongxin Wu2Jian Ding3School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaEnsemble classification is widely used in multi-label algorithms, and it can be divided into homogeneous ensembles and heterogeneous ensembles according to classifier types. A heterogeneous ensemble can generate classifiers with better diversity than a homogeneous ensemble and improve the performance of classification results. An Adaptive Heterogeneous Classifier Group (AHCG) algorithm is proposed. The AHCG first proposes the concept of a Heterogeneous Classifier Group (HCG); that is, two groups of different ensemble classifiers are used in the testing and training phases. Secondly, the Adaptive Selection Strategy (ASS) is proposed, which can select the ensemble classifiers to be used in the test phase. The least squares method is used to calculate the weights of the base classifiers for the in-group classifiers and dynamically update the base classifiers according to the weights. A large number of experiments on seven datasets show that this algorithm has better performance than most existing ensemble classification algorithms in terms of its accuracy, example-based F1 value, micro-averaged F1 value, and macro-averaged F1 value.https://www.mdpi.com/2227-7390/13/1/103multi-label classificationheterogeneous ensembleheterogeneous classifier groupadaptive selection strategydynamic update |
| spellingShingle | Meng Han Shurong Yang Hongxin Wu Jian Ding Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group Mathematics multi-label classification heterogeneous ensemble heterogeneous classifier group adaptive selection strategy dynamic update |
| title | Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group |
| title_full | Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group |
| title_fullStr | Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group |
| title_full_unstemmed | Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group |
| title_short | Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group |
| title_sort | multi label classification algorithm for adaptive heterogeneous classifier group |
| topic | multi-label classification heterogeneous ensemble heterogeneous classifier group adaptive selection strategy dynamic update |
| url | https://www.mdpi.com/2227-7390/13/1/103 |
| work_keys_str_mv | AT menghan multilabelclassificationalgorithmforadaptiveheterogeneousclassifiergroup AT shurongyang multilabelclassificationalgorithmforadaptiveheterogeneousclassifiergroup AT hongxinwu multilabelclassificationalgorithmforadaptiveheterogeneousclassifiergroup AT jianding multilabelclassificationalgorithmforadaptiveheterogeneousclassifiergroup |