Zdrojový dokument:Procedia Computer Science : 24th KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems KES2020
Název akce24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2020 (16.09.2020 - 18.09.2020, ONLINE)
Abstrakt:
In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach.