A modified NEFCLASS classifier with enhanced accuracy-interpretability trade-off for datasets with skewed feature values
| Autor: | Jamileh Yousefi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Skewed data
0209 industrial biotechnology Discretization Logic business.industry Pattern recognition 02 engineering and technology Residual Combined approach 020901 industrial engineering & automation Artificial Intelligence Skewness 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Medical diagnosis business Classifier (UML) Mathematics Interpretability |
| Zdroj: | Fuzzy Sets and Systems. 413:99-113 |
| ISSN: | 0165-0114 |
| DOI: | 10.1016/j.fss.2020.07.011 |
| Popis: | The accuracy-transparency trade-off is one of the most notable challenges when applying machine learning tools in the medical domain. Nefclass is a popular neuro-fuzzy classifier in medical diagnosis systems. Nefclass performs increasingly poorly as the data skewness increases. This paper presents a combined approach to improve the classification accuracy and interpretability of the Nefclass classifier, when feature values of the training and testing datasets exhibit positive skewness. The proposed model consists of two steps. Firstly, a modified Nefclass classifier embedded with a choice of two alternative discretization methods, MME and CAIM is implemented. Secondly, we devised a new rule pruning method based on the Habermans' adjusted residual to reduce the size of the resulting ruleset. This rule-pruning method improves the interpretability of Nefclass without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches provides a considerable improvement in classification accuracy and transparency of Nefclass on skewed data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect