Abstract
Classification is one of the tasks that are most frequently carried out in real world applications. A large number of techniques have been developed based on statistics and machine learning methods. These classification techniques usually suffer from various limitations, and there is no single technique that works best for all classification problems. Two major drawbacks in existing techniques are accuracy and lack of actionable knowledge from results. To overcome these problems, a novel algorithm called Multi-Branch Ferns (MBFerns), and R-package has been developed to build multi-branch ferns (multi-branch decision tree) and to generate key features from training dataset employing Naïve Bayesian probabilistic model as classifier. The proposed algorithm performs well for general classification problems and extracting actionable knowledge from training data. The proposed method has been evaluated with best existing classification methods namely, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) on medical benchmark data, available at https://archive.ics.uci.edu/ml/datasets/ such as Breast Cancer, Cryotherapy, Cardiotocography, Dermatology, Echocardiogram, EEG Eye State, Fertility, Haberman's Survival, Hepatitis, Indian Liver Patient, Mammographic Mass, Parkinsons, etc. Detailed investigation on proposed Multi-Branch Ferns (MBFerns) with respect to accuracy, time, space complexity and knowledge discovery has also been presented.
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Acknowledgments
Authors are thankful to Indian Council of Agricultural Research, Ministry of Agriculture and Farmers’ Welfare, Govt. of India for providing financial assistance. The authors are very grateful to the anonymous reviewers for their insightful comments to improve the manuscript
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CABin scheme grant (FN/Agril-Edn./4-1/2013-A&P) Indian Council of Agricultural Research, Ministry of Agriculture and Farmers’ Welfare, Govt. of India.
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Angadi UB and Anil Rai conceived and designed the method; Angadi UB implemented and developed R-package; Uma G and Angadi UB analyzed the algorithm and R-package with benchmark data; Angadi UB and Anil Rai wrote the paper. All the authors read and approved the manuscript.
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Angadi, U.B., Rai, A. & Uma, G. MBFerns: classification and extraction of actionable knowledge using Multi-Branch Ferns-based Naive Bayesian classifier. Soft Comput 25, 8357–8369 (2021). https://doi.org/10.1007/s00500-021-05759-5
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DOI: https://doi.org/10.1007/s00500-021-05759-5