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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| 主要な著者: | , , |
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| フォーマット: | Conference poster |
| 言語: | English |
| 出版事項: |
IEEE
2023
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| 主題: | |
| オンライン･アクセス: | https://scholar.dlu.edu.vn/handle/123456789/2711 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| 要約: | 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. |
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