A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification
Feature selection is a useful technique in classification (and regression) problems to find the most informative features for predicting but still preserves the data generality. However, some feature subset searching methods are too exhaustive while others are too greedy. On the other hand, paramete...
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2023
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2711 |
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oai:scholar.dlu.edu.vn:123456789-27112023-06-14T17:13:49Z A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification Phạm, Quang Huy Alioune Ngom Luis Rueda machine learning feature selection Feature selection is a useful technique in classification (and regression) problems to find the most informative features for predicting but still preserves the data generality. However, some feature subset searching methods are too exhaustive while others are too greedy. On the other hand, parameter searching is another factor to improve the prediction performance. But, if it is conducted separately after feature selection stage the classification model might not be as optimal as it should. In this study, we propose a new method, called Apriori-like Feature Selection that can overcome those drawbacks. Given a classifier and a dataset, it searches for the optimal parameters and the optimal feature subset in the combined space of features and parameters. Moreover, its greedy search behavior is controllable by running options. When applying this approach on a breast cancer dataset of five subtypes, it yielded the overall classification accuracy of more than 99% but requires only about 12 genes; a significant improvement as compared to another study. 2023-06-14T17:13:40Z 2023-06-14T17:13:40Z 2016 Conference poster Bài báo đăng trên KYHT quốc tế (có ISBN) https://scholar.dlu.edu.vn/handle/123456789/2711 10.1109/BIBM.2016.7822749 en 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) IEEE |
| institution |
Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
machine learning feature selection |
| spellingShingle |
machine learning feature selection Phạm, Quang Huy Alioune Ngom Luis Rueda A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| description |
Feature selection is a useful technique in classification (and regression) problems to find the most informative features for predicting but still preserves the data generality. However, some feature subset searching methods are too exhaustive while others are too greedy. On the other hand, parameter searching is another factor to improve the prediction performance. But, if it is conducted separately after feature selection stage the classification model might not be as optimal as it should. In this study, we propose a new method, called Apriori-like Feature Selection that can overcome those drawbacks. Given a classifier and a dataset, it searches for the optimal parameters and the optimal feature subset in the combined space of features and parameters. Moreover, its greedy search behavior is controllable by running options. When applying this approach on a breast cancer dataset of five subtypes, it yielded the overall classification accuracy of more than 99% but requires only about 12 genes; a significant improvement as compared to another study. |
| format |
Conference poster |
| author |
Phạm, Quang Huy Alioune Ngom Luis Rueda |
| author_facet |
Phạm, Quang Huy Alioune Ngom Luis Rueda |
| author_sort |
Phạm, Quang Huy |
| title |
A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| title_short |
A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| title_full |
A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| title_fullStr |
A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| title_full_unstemmed |
A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| title_sort |
new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification |
| publisher |
IEEE |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2711 |
| _version_ |
1778233930880647168 |