Читать книгу Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh - Страница 37

Bayes optimal classifier is a popular machine learning model used for the purpose of prediction. This technique is based on Bayes theorem which is principled by Bayes theorem and closely related to maximum posteriori algorithm. The classifier operates by finding the hypothesis which has maximum probability of occurrence. The probable prediction is carried out by the classifier using probabilistic model which finds the most probable prediction using the training and testing data instances.

The basic conditional probability equation predicts one outcome given another outcome, consider A and B are two probable outcomes the probability of occurrence of event using the equation P(A|B) = (P(B|A)*P(A))/P(B). The probabilistic frameworks used for prediction purpose are broadly classified into two types one is maximum posteriori, and the other is maximum likelihood estimation. The important objective of these two types of probabilistic framework is that they locate most promising hypothesis in the given training data sample. Some of the zonotic diseases which can be identified and treated well using Bayes optimal classifier are Anthrax, Brucellosis, Q fever, scrub typhus, plague, tuberculosis, leptospirosis, rabies, hepatitis, nipah virus, avian influenza, and so on [12, 13]. A high-level representation of Bayes optimal classifier is shown in Figure 2.1. In the hyperplane of available datasets, the Bayes classifier performs the multiple category classification operation to draw soft boundary among the available datasets and make separate classifications. It is observed that, with maximum iteration of training and overtime, the accuracy of the Bayes optimal classifier keeps improving.

Some of the advantages of advantages of Bayes optimal classifier which makes it suitable for tracking and solving the zonotic diseases are as follows: ease of implementation, high accuracy is achieved over less training data, capable of handling both discrete and non-discrete data samples, scalable to any number of data samples, operates at very speed, suitable for real-time predictions, achieves better results compared to traditional classifiers, not much sensitive to outliers, ease generalization, achieves high computational accuracy, works well on linear/nonlinear separable data samples, interpretation of the results is easy, easily mines the complex relationship between input and output data samples, provides global optimal solutions, and so on [14].

Figure 2.1 A high-level representation of Bayes optimal classifier.