Erythemato Squamous Disease Prediction using Ensemble Multi-Feature Selection Approach
| Main Authors: | Efosa Charles, Igodan, Aderonke Favour-Bethy, Thompson, Olumide, Obe, Otasowie, Owolafe |
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| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2022
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/6380653 |
Daftar Isi:
- Skin disease dataset has six classes characterized with redundant and noisy features making classification very difficult due to similarities among the classes. To obtain relevant features to the target concept has being a major component in data mining processes. Although, there is no “one-fit-all” model that outperform all others, in this paper, we described a new methodology that combines four different filtering and three embedded feature selection methods to obtain optimal features for individual models and their ensemble for skin disease. On Dermatology datasets, the proposed ensemble method, which is based on machine learning, was able to classify skin disease types into six categories using the one vs many classification approach. The results show that the stacked ensemble obtained 92.9% accuracy, 85.8% sensitivity and 97.4% specificity compared to both single and ensemble classifiers. This paper proves that ensemble learning methods predict skin disease more accurately and effectively. Keywords: Filtering Method; Embedded Method; Ensemble Learning; SVMs and Erythemato Squamous Disease