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- W4383562377 abstract "In a classification problem, before building a prediction model, it is very important to identify informative features rather than using tens or thousands which may penalize some learning methods and increase the risk of over-fitting. To overcome these problems, the best solution is to use feature selection. In this article, we propose a new filter method for feature selection, by combining the Relief filter algorithm and the multi-criteria decision-making method called TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), we modeled the feature selection task as a multi-criteria decision problem. Exploiting the Relief methodology, a decision matrix is computed and delivered to Technique for Order Preference by Similarity to Ideal Solution in order to rank the features. The proposed method ends up giving a ranking to the features from the best to the mediocre. To evaluate the performances of the suggested approach, a simulation study including a set of experiments and case studies was conducted on three synthetic dataset scenarios. Finally, the obtained results approve the effectiveness of our proposed filter to detect the best informative features." @default.
- W4383562377 created "2023-07-08" @default.
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- W4383562377 date "2023-01-01" @default.
- W4383562377 modified "2023-09-25" @default.
- W4383562377 title "A filter feature selection for high-dimensional data" @default.
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- W4383562377 doi "https://doi.org/10.1177/17483026231184171" @default.
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