An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks
Furkejuvvon:
| Publikašuvnnas: | arXiv.org (Dec 13, 2013), p. n/a |
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| Váldodahkki: | |
| Eará dahkkit: | |
| Almmustuhtton: |
Cornell University Library, arXiv.org
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full text outside of ProQuest |
| Fáddágilkorat: |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2085807521 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2085807521 | ||
| 045 | 0 | |b d20131213 | |
| 100 | 1 | |a Smith, Michael R | |
| 245 | 1 | |a An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 13, 2013 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which learning algorithms to use for filtering as well as their effects on multiple learning algorithms over a large set of data sets has not been done. Previous work has generally been limited due to the large computational requirements to run such an experiment, and, thus, the examination has generally been limited to learning algorithms that are computationally inexpensive and using a small number of data sets. In this paper, we examine 9 learning algorithms as filtering algorithms as well as examining the effects of filtering in the 9 chosen learning algorithms on a set of 54 data sets. In addition to using each learning algorithm individually as a filter, we also use the set of learning algorithms as an ensemble filter and use an adaptive algorithm that selects a subset of the learning algorithms for filtering for a specific task and learning algorithm. We find that for most cases, using an ensemble of learning algorithms for filtering produces the greatest increase in classification accuracy. We also compare filtering with a majority voting ensemble. The voting ensemble significantly outperforms filtering unless there are high amounts of noise present in the data set. Additionally, we find that a majority voting ensemble is robust to noise as filtering with a voting ensemble does not increase the classification accuracy of the voting ensemble. | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Algorithms | ||
| 653 | |a Classification | ||
| 653 | |a Outliers (statistics) | ||
| 653 | |a Machine learning | ||
| 653 | |a Adaptive filters | ||
| 653 | |a Adaptive algorithms | ||
| 700 | 1 | |a Martinez, Tony | |
| 773 | 0 | |t arXiv.org |g (Dec 13, 2013), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2085807521/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1312.3970 |