A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance
Classifiers today face numerous challenges, including overfitting, high computational costs, low accuracy, imbalanced datasets, and lack of interpretability. Additionally, traditional methods often struggle with noisy or missing data. To address these issues, we propose novel classification methods...
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MDPI AG
2024-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/7 |
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| author | Nabil Belacel |
| author_facet | Nabil Belacel |
| author_sort | Nabil Belacel |
| collection | DOAJ |
| description | Classifiers today face numerous challenges, including overfitting, high computational costs, low accuracy, imbalanced datasets, and lack of interpretability. Additionally, traditional methods often struggle with noisy or missing data. To address these issues, we propose novel classification methods based on feature partitioning and outranking measures. Our approach eliminates the need for prior domain knowledge by automatically learning feature intervals directly from the data. These intervals capture key patterns, enhancing adaptability and insight. To improve robustness, we incorporate outranking measures, which reduce the impact of noise and uncertainty through pairwise comparisons of alternatives across features. We evaluate our classifiers on multiple UCI repository datasets and compare them with established methods, including k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NNs), Naive Bayes (NB), and Nearest Centroid (NC). The results demonstrate that our methods are robust to imbalanced datasets and irrelevant features, achieving comparable or superior performance in many cases. Furthermore, our classifiers offer enhanced interpretability while maintaining high predictive accuracy. |
| format | Article |
| id | doaj-art-73b1c019557c4e51a77d37259216f567 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-73b1c019557c4e51a77d37259216f5672025-01-24T13:17:26ZengMDPI AGAlgorithms1999-48932024-12-01181710.3390/a18010007A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved PerformanceNabil Belacel0Digital Technologies Research Center, National Research Council Canada, Ottawa, ON K1A 0R6, CanadaClassifiers today face numerous challenges, including overfitting, high computational costs, low accuracy, imbalanced datasets, and lack of interpretability. Additionally, traditional methods often struggle with noisy or missing data. To address these issues, we propose novel classification methods based on feature partitioning and outranking measures. Our approach eliminates the need for prior domain knowledge by automatically learning feature intervals directly from the data. These intervals capture key patterns, enhancing adaptability and insight. To improve robustness, we incorporate outranking measures, which reduce the impact of noise and uncertainty through pairwise comparisons of alternatives across features. We evaluate our classifiers on multiple UCI repository datasets and compare them with established methods, including k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NNs), Naive Bayes (NB), and Nearest Centroid (NC). The results demonstrate that our methods are robust to imbalanced datasets and irrelevant features, achieving comparable or superior performance in many cases. Furthermore, our classifiers offer enhanced interpretability while maintaining high predictive accuracy.https://www.mdpi.com/1999-4893/18/1/7machine learningclassificationsupervised learningfeature interval learningoutranking measures |
| spellingShingle | Nabil Belacel A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance Algorithms machine learning classification supervised learning feature interval learning outranking measures |
| title | A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance |
| title_full | A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance |
| title_fullStr | A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance |
| title_full_unstemmed | A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance |
| title_short | A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance |
| title_sort | closest resemblance classifier with feature interval learning and outranking measures for improved performance |
| topic | machine learning classification supervised learning feature interval learning outranking measures |
| url | https://www.mdpi.com/1999-4893/18/1/7 |
| work_keys_str_mv | AT nabilbelacel aclosestresemblanceclassifierwithfeatureintervallearningandoutrankingmeasuresforimprovedperformance AT nabilbelacel closestresemblanceclassifierwithfeatureintervallearningandoutrankingmeasuresforimprovedperformance |