ITM Web Conf.
Volume 36, 2021The 16th IMT-GT International Conference on Mathematics, Statistics and their Applications (ICMSA 2020)
|Number of page(s)
|Statistics and Data Science
|26 January 2021
The new baseline for high dimensional dataset by ranked mutual information features
Department of Physical and Mathematical Science, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia
2 Department of Mathematical and Actuarial Sciences, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia
* Corresponding author: firstname.lastname@example.org
Feature selection is a process of selecting a group of relevant features by removing unnecessary features for use in constructing the predictive model. However, high dimensional data increases the difficulty of feature selection due to the curse of dimensionality. From the past research, the performance of the predictive model is always compared with the existing results. When attempting to model a new dataset, the current practice is to benchmark for the dataset obtained by including all the features, including redundant features and noise. Here we propose a new optimal baseline for the dataset by mean of ranked features using a mutual information score. The quality of a dataset depends on the information contained in the dataset, and the more information contains in the dataset, the better the performance of the predictive model. The number of features to achieve this new optimal baseline will be obtained at the same time, and serve as the guideline on the number of features needed in a feature selection method. We will also show some experimental results that the proposed method provides a better baseline with fewer features compared to the existing benchmark using all the features.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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