Mathematical models of classification algorithm of Machine learning

Dutta Nabanita (1) , Subramaniam Umashankar (2) , Padmanaban Sanjeevikumar (3)

1 School of Electrical Engineering [Vellore]
2 Prince Sultan university (saudi Arabia), Riyadh
3 AAU - Aalborg University [Denmark]

Machine learning algorithm has brought the augmenting change in the field of artificial intelligence which espoused human discerning power in a splendid manner. The algorithm has various categories among which classification is the most popular part. Support vector machine algorithm, logistic regression, naïve bays algorithm, decision tree, boosted tree, random forest and k nearest neighbour algorithm all are under classification algorithms. Classification process needs some pre-defined method which leads the method for choosing the train data from the sample data given by the user. Decision making is the heart of any classification algorithm as supervised learning stands out on the decision of users. So the strong mathematical model based on conditional probability lies behind each algorithm. This paper is the study of those mathematical models and logic behind various classification algorithms which help to create a strong decision for users to make the training dataset based on which machine can predict the proper output

Domaines: Intelligence artificielle [cs.AI]

Dutta Nabanita, Subramaniam Umashankar, Padmanaban Sanjeevikumar. Mathematical models of classification algorithm of Machine learning. International Meeting on Advanced Technologies in Energy and Electrical Engineering (IMAT3E'18), Nov 2018, Fez, Morocco. ⟨hal-01941420v2⟩