Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure
A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only b...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/961747 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562151373406208 |
|---|---|
| author | Xiaodong Zeng Derek F. Wong Lidia S. Chao |
| author_facet | Xiaodong Zeng Derek F. Wong Lidia S. Chao |
| author_sort | Xiaodong Zeng |
| collection | DOAJ |
| description | A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases. |
| format | Article |
| id | doaj-art-3e0323278f8b4ab382017f9beec6dced |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-3e0323278f8b4ab382017f9beec6dced2025-02-03T01:23:19ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/961747961747Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity MeasureXiaodong Zeng0Derek F. Wong1Lidia S. Chao2NLP2CT Lab/Department of Computer and Information Science, University of Macau, Taipa 999078, MacauNLP2CT Lab/Department of Computer and Information Science, University of Macau, Taipa 999078, MacauNLP2CT Lab/Department of Computer and Information Science, University of Macau, Taipa 999078, MacauA weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.http://dx.doi.org/10.1155/2014/961747 |
| spellingShingle | Xiaodong Zeng Derek F. Wong Lidia S. Chao Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure The Scientific World Journal |
| title | Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure |
| title_full | Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure |
| title_fullStr | Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure |
| title_full_unstemmed | Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure |
| title_short | Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure |
| title_sort | constructing better classifier ensemble based on weighted accuracy and diversity measure |
| url | http://dx.doi.org/10.1155/2014/961747 |
| work_keys_str_mv | AT xiaodongzeng constructingbetterclassifierensemblebasedonweightedaccuracyanddiversitymeasure AT derekfwong constructingbetterclassifierensemblebasedonweightedaccuracyanddiversitymeasure AT lidiaschao constructingbetterclassifierensemblebasedonweightedaccuracyanddiversitymeasure |