Abstract
Feature selection is a process of preparing data to be more effective and efficient for machine learning problems. The purpose of feature selection is to select relevant features from huge number of features. To build a simple model which will be easy to understand data and take less time to train the model, thereby optimizing model performance. The paper proposes two feature selection techniques namely Lasso and Select From Model (meta-transformer) to select relevant features from flag dataset that intensifies the model performance. For the prediction of religion of a country, three tree-based classifiers are used—random forest, decision tree, and extra trees model. Among these, random forest classifier gives best prediction.
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References
Kutlay, M.A., Yaman, E.: Comparison of different machine learning algorithms for national flags classification” Southeast Eur. J. Soft Comput. 4(2) (2015). ISSN 2233–1859
Khand, M.A.H.A., Al-Mahmud, I.H., Murase, K.: Knowledge discovery from national flag through data mining approach. Int. J. Knowl. Eng. Res. 2(4) (2013) ISSN 2319–832X
Dash, S.R., Sheeraz, A.S., Samantaray, A.: Filtration and classification of ECG signals. In: Handbook of Research on Information Security in Biomedical Signal Processing, pp. 72–94. IGI Global (2018)
Zeng, X.-Q., Li, G.-Z.: Supervised redundant feature detection for tumor classification. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013) Shanghai, China, 18–21 December 2013
UCI Machine Learning Repository: Flags Data Set. https://archive.ics.uci.edu/ml/datasets/Flags
Feature Selection For Machine Learning in Python. https://machinelearningmastery.com/feature-selection-machine-learning-python
Introduction to Feature Selection Methods. https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables
Dash, S.R., Sahu, R.: Prediction of death rate using regression analysis. In: Emerging Technologies in Data Mining and Information Security, pp. 735–745. Springer, Singapore (2019)
Feature Selection. http://scikit-learn.org/stable/modules/feature_selection.html
Choosing Right Features—Feature Importance and Selection. https://www.fabienplisson.com/choosing-right-features
Farid, D.M., Zhang, L., Rahman, C.M., Hossain, M.A., Strachan, R.: Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks. Expert Syst. Appl. 41, 1937–1946 (2014)
Abellán, J., Mantas, C.J., Castellano, J.G.: A random forest approach using imprecise probabilities. Knowl.-Based Syst. 134, 72–84 (2017)
Difference between Random Forest and Extremely Randomized Trees. https://stats.stackexchange.com/questions/175523/difference-between-random-forest-and-extremely-randomized-trees
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Samantaray, A., Dash, S.R. (2020). Feature Selection Techniques to Predict the Religion of a Country from Its Flag. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 159. Springer, Singapore. https://doi.org/10.1007/978-981-13-9282-5_18
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DOI: https://doi.org/10.1007/978-981-13-9282-5_18
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