Naive Bayesian classifier

Three algorithms have been implemented in both single and multiperspective environments. In this way any bias introduced by a single algorithm should be removed. The first is the statistical Naive Bayesian Classifier, ft reduces the decision-making problem to simple calculations of feature probabilities, ft is based on Bayes theorem and calculates the posterior probability of classes conditioned on the given unknown feature... 

Naive Bayesian classifier Intestinal absorption (passive), blood-brain barrier penetration, serum protein binding Classifier No [17]... 

Glick, M. et al. 2006. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian-modified naive Bayesian classifiers. J. Chem. Inf. Model. 46, 193-200. 

Bender, A., Mussa, H.Y., Glen, R.C. and Reihng, S. (2004b) Molecular similarity searching using atom environments, information-based feature selection, and a naive Bayesian classifier. /. Chem. Inf. Comput. Sci., 44, 170-178. 

Konenko I. Semi-naive Bayesian classifier. EWSL-91 Proceedings of the European Working Session on Learning on Machine Learning. Heidelberg Springer 1991. p 206-219. 

The Bayesian approach is one of the probabilistic central parametric classification methods it is based on the consistent apphcation of the classic Bayes equation (also known as the naive Bayes classifier ) for conditional probabihty [34] to constmct a decision rule a modified algorithm is explained in references [105, 109, 121]. In this approach, a chemical compound C, which can be specified by a set of probability features (Cj,...,c ) whose random values are distributed through all classes of objects, is the object of recognition. The features are interpreted as independent random variables of an /w-dimensional random variable. The classification metric is an a posteriori probability that the object in question belongs to class k. Compound C is assigned to the class where the probability of membership is the highest. 

Five methods of feature selection (information gain, mutual information, X -test, odds ratio, and GSS coefficient) were compared by Liu for their ability to discriminate between thrombin inhibitors and noninhibitors.The chemical compounds were provided by DuPont Pharmaceutical Research Laboratories as a learning set of 1909 compounds contained 42 inhibitors and 1867 noninhibitors, and a test set of 634 compounds contained 150 inhibitors and 484 noninhibitors. Each compound was characterized by 139,351 binary features describing their 3-D structure. In this comparison of naive Bayesian and SVM classifiers, all compounds were considered together, and a L10%O cross-validation procedure was applied. Based on information gain descriptor selection,... 

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