A Supervised Classification Techniques to Optimize Error Evaluation and Space Complexity
M. Santhiya1, M. Shobana2, R. Jegatha3
1M. Santhiya, Assistant Professor, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
2M. Shobana, Assistant Professor, Saveetha Engineering College, Chennai (Tamil Nadu), India.
3R. Jegatha, Assistant Professor, Sri Sairam Institute of Technology, West Thambaram, Chennai (Tamil Nadu), India.
Manuscript received on 08 September 2019 | Revised Manuscript received on 17 September 2019 | Manuscript Published on 11 October 2019 | PP: 92-95 | Volume-8 Issue-11S September 2019 | Retrieval Number: K102009811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1020.09811S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Bayesian classification is based on Baye’s Theorem, which is applied on a conditional probability basis of posterior and prior probabilities in parallel with future evidence. Prior Probabilities are the original probabilities of an outcome which will be updated with new information to create posterior probability. The revised probability of an event occurring after taking into consideration new information. A Bayesian classifier is used to predict the values of features for members of that class. It is used to over come the diagnostic and predictive problems. This classification provides a useful perspective for understanding and evaluating machine learning algorithms. It is a probabilistic learning algorithm which calculates the explicit probabilities for hypothesis, among the most common learning problem. The proposed work has focused on designing of two classification algorithms naïve space and naïve Mine classification to optimize space complexity and error evaluation for larger data sets.
Keywords: Prior & Posterior Probability, Bayes Theorem, Naive Space, Naive Mine.
Scope of the Article: Classification