The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Feature Selection for Classification using Principal Component Analysis and Information Gain
Abstract
Abstract Feature Selection and classification have previously been widely applied in various areas like business, medical and media fields. High dimensionality in datasets is one of the main challenges that has been experienced in classifying data, data mining and sentiment analysis. Irrelevant and redundant attributes have also had a negative impact on the complexity and operation of algorithms for classifying data. Consequently, the algorithms record poor results or performance. Some existing work use all attributes for classification, some of which are insignificant for the task, thereby leading to poor performance. This paper therefore develops a hybrid filter model for feature selection based on principal component analysis and information gain. The hybrid model is then applied to support classification using machine learning techniques e.g. the Naive Bayes technique. Experimental results demonstrate that the hybrid filter model reduces data dimensions, selects appropriate feature sets, and reduces training time, hence providing better classification performance as measured by accuracy, precision and recall..
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Cites
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Cited by
Adapting Feature Selection Algorithms for the Classification of Chinese Texts
Liu, X., Wang, S., Lu, S., Yin, Z., Li, X., Yin, L., Tian, J., & Zheng, W. (2023) · Systems · doi:10.3390/systems11090483 · Cited by 336
Gong, H., Li, Y., Zhang, J., Zhang, B., & Wang, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107865 · Cited by 331
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“Omuya et al. [414] prove that by comparing four different scenarios, data that has gone through the PCA process has the best performance.”
Background
Metadata
- Authors
- Odhiambo Omuya, E., Onyango Okeyo, G., & Waema Kimwele, M.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 174
- Pages
- 114765
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W3135387830
- Cited by
- 398
- References
- 31
- FWCI
- 21.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- Odhiambo Omuya, E., Onyango Okeyo, G., & Waema Kimwele, M. (2021). Feature Selection for Classification using Principal Component Analysis and Information Gain. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2021.114765
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