Fitting Multiple Machine Learning Models with Performance Based Clustering
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917906381537280 |
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| author | Lorasdagi, Mehmet Efe Koc, Ahmet Berker Koc, Ali Taha Kozat, Suleyman Serdar |
| author_facet | Lorasdagi, Mehmet Efe Koc, Ahmet Berker Koc, Ali Taha Kozat, Suleyman Serdar |
| contents | Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the features and the target values and we obtain multiple separate models to learn different parts of the data. We further extend our framework to applications having streaming data where we produce outcomes using an ensemble of models. For this, the ensemble weights are updated based on the incoming data batches. We demonstrate the performance of our approach over the widely-studied real life datasets, showing significant improvements over the traditional single-model approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_06572 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fitting Multiple Machine Learning Models with Performance Based Clustering Lorasdagi, Mehmet Efe Koc, Ahmet Berker Koc, Ali Taha Kozat, Suleyman Serdar Machine Learning Signal Processing Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the features and the target values and we obtain multiple separate models to learn different parts of the data. We further extend our framework to applications having streaming data where we produce outcomes using an ensemble of models. For this, the ensemble weights are updated based on the incoming data batches. We demonstrate the performance of our approach over the widely-studied real life datasets, showing significant improvements over the traditional single-model approaches. |
| title | Fitting Multiple Machine Learning Models with Performance Based Clustering |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2411.06572 |