Abstract
This study aims to implement the following four advanced pattern recognition algorithms, such as “optimal Bayesian classifier,” “anti-Bayesian classifier,” “decision trees (DTs),” and “dependence trees (DepTs)” on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the fourth section, a comparison between the classification accuracy of DT and DepT classification methods for both the artificial and real-life datasets is discussed. In the fifth section, a comparison between the classification accuracy of the four algorithms, such as (a) Bayes, (b) anti-Bayes, (c) DTs, and (d) DepTs for both the artificial and real datasets is explained. We used 5-fold cross-validation to determine the classification accuracy of individual, machine learning-based, advanced pattern recognition (PR) models.
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Acknowledgements
Thanks to Dr. John Oommen (Chancellor’s Professor, School of Computer Science, Carleton University, Ottawa, Ontario K1S 5B6, Canada) for teaching us the course on “Advanced Pattern Recognition” as a part of PhD coursework, and reviewing the article which is written based on the assignment of the coursework.
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Chatterjee, A., Gerdes, M.W., Prinz, A., Martinez, S. (2021). A Comparative Study to Analyze the Performance of Advanced Pattern Recognition Algorithms for Multi-Class Classification. In: Tavares, J.M.R.S., Chakrabarti, S., Bhattacharya, A., Ghatak, S. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-15-9774-9_11
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