Abstract
A significant growth of digital data in different applications increases the data size and storage. Digital data may include missing values, irrelevant information, incorrect values, and redundant features. Each attribute in data collection is called features of dimensions. More dimensions in a dataset make prediction a complicated task. Feature selection is a method that plays a vital role in reducing the dimension of data and it can be done as an initial step in processing. Feature algorithm extract the refined feature for better classification and accuracy of predictive models. The proper selection of features is used to increase the efficiency of a dataset and performance of a model. Feature selection methods are not only used to reduce dataset but also to reduce the overfitting problems in mining process. This paper presents various feature selection methods in order to extract consistent data. The algorithms such as CFS, CAE, IGE, GRE, and WSE are used to select features. To measure the performance of these selected feature Naive Bayes (NB) model and support vector machine (SVM) model are used. Experimental result shows CAE, GRE, and IGE with SVM model give better performance than other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bermejo S (2017) Ensembles of wrappers for automated feature selection in fish age classification. Comput Electron Agric 134:27–32
Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453
Kadhim BS, Janabi A, Kadhim R (2018) Data reduction techniques: a comparative study for attribute selection methods. Int J Adv Comp Sci Tech 8(1):1–13. ISSN 2249-3123 © Research India Publications http://www.ripublication.com
Rozlini M (2018) Munirah Mohd Yusof and Noorhaniza Wahidi”, A comparative study of feature selection techniques for Bat algorithm in various applications. MATEC Web of Conferences 150:06006. https://doi.org/10.1051/matecconf/201815006006
Venkatesh B, Anuradha J (2019) A review of feature selection and its methods. Cybernetics Inform Technol 19. ISSN: 1311-9702; Online ISSN: 1314-4081. https://doi.org/10.2478/cait-2019-0001
Hasri NM, Wen NH, Howe CW, Mohamad MS, Deris S, Kasim S (2017) Improved support vector machine using multiple SVM-RFE for cancer classification. Int J Adv Sci Eng Inf Technol 7:1589–1594
Shah SAA, Shabbir HM, et al (2020) A comparative study of feature selection approaches: 2016–2020. Int J Scient Eng Res 11(2), February. ISSN 2229-5518
Das S, Singh PK, Bhowmik S, Sarkar R, Nasipuri M (2017) A harmony search based wrapper feature selection method for holistic Bangla word recognition. Procedia Comput Sci 89:395–403
Liu Z, Wang R, Japkowicz N et al (2019) Mobile app traffic flow feature extraction and selection for improving classification robustness. J Netw Comput Appl 125:190–208. https://doi.org/10.1016/j.jnca.2018.10.018
Kang C, Huo Y et al (2019) Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine. J Theor Biol 463:77–91. https://doi.org/10.1016/j.jtbi.2018.12.010
Venkatesh B, Anuradha J, A review of feature selection and its methods. Cybernetics Inform Technol 19(1). Print ISSN: 1311-9702; Online ISSN: 1314-4081. https://doi.org/10.2478/cait-2019-0001.
Rahman MA, Muniyandi RC (2018) Feature selection from colon cancer dataset for cancer classification using artificial neural network. Int J Adv Sci Eng Inf Technol 8:1387–1393
Li J, Cheng K, Wang S, Morstatter F (2018) Feature selection: a data perspective. ACM Comp
Jameel S, Rehman SU (2018) An optimal feature selection method using a modified wrapper-based ant colony optimisation. J Nat Sci Foundation of Sri Lanka 46(2)
Wang H, Zheng B, Yoon SW, Ko HS (2018) A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 267:687–699
Pratiwi AI, Adiwijaya (2018) On the feature selection and classification based on information gain for document sentiment analysis. Hindawi Appl Comput Intell Soft Comp, Article ID 1407817, 5 p. https://doi.org/10.1155/2018/1407817
Gnanambal S, Thangaraj M et al (2018) Classification algorithms with attribute selection: an evaluation study using WEKA. Int J Adv Networking Appl 9:3640–3644, 6 p. ISSN: 0975-0290
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Usha, P., Anuradha, M.P. (2023). An Evaluation of Feature Selection Methods Performance for Dataset Construction. In: Subhashini, N., Ezra, M.A.G., Liaw, SK. (eds) Futuristic Communication and Network Technologies. Lecture Notes in Electrical Engineering, vol 966. Springer, Singapore. https://doi.org/10.1007/978-981-19-8338-2_9
Download citation
DOI: https://doi.org/10.1007/978-981-19-8338-2_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8337-5
Online ISBN: 978-981-19-8338-2
eBook Packages: EngineeringEngineering (R0)