Classification is an important data mining technique that is used by many applications. Several types of classifiers have been described in the research literature. Example classifiers are decision tree classifiers, rule-based classifiers, and neural networks classifiers. Another popular classification technique is naïve Bayesian classification. Naïve Bayesian classification is a probabilistic ...

The amount of data present online is growing very rapidly, hence a need for organizing and categorizing data has become an obvious need. The Information Retrieval (IR) techniques act as an aid in assisting users in obtaining relevant information. IR in the Indian context is very relevant as there are several blogs, news publications in Indian languages present online. This work looks at the sui...

In this paper, we demonstrate how semantic categories of images can be learnt from their color distributions using an effective probabilistic approach. Many previous probabilistic approaches are based on the Naïve Bayes that assume independence among attributes, which are represented by a single Gaussian distribution. We use a derivative of the Naïve Bayesian classifier, called Flexible Bayesia...

Naïve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of str...

The apparent difficulty people have with making Bayesian inferences has been researched heavily over the past 25 years, with conflicting explanations regarding the causes of and the cures for this inadequacy. Some researchers have improved Bayesian reasoning by representing the problem visually, but usually as a tool to teach Bayesian reasoning skills. This research examines facilitating reason...

In order to improve the ability of gradual learning on the training set gotten in batches of Naive Bayesian classifier, an incremental Naïve Bayesian learning algorithm is improved with the research on the existing incremental Naïve Bayesian learning algorithms. Aiming at the problems with the existing incremental amending sample selection strategy, the paper introduced the concept of sample Cl...

This paper introduces a new Bayesian network structure, named a Partial Bayesian Network (PBN), and describes an algorithm for constructing it. The PBN is designed to be used for classification tasks, and accordingly the algorithm constructs an approximate Markov blanket around a classification node. Initial experiments have compared the performance of the PBN algorithm with Naïve Bayes, Tree-A...

This paper proposes a new approach based on augmented naive Bayes for image classification. Initially, each image is cutting in a whole of blocks. For each block, we compute a vector of descriptors. Then, we propose to carry out a classification of the vectors of descriptors to build a vector of labels for each image. Finally, we propose three variants of Bayesian Networks such as Naïve Bayesia...

Diabetic retinopathy (DR) is an eye disease caused by disease, diabetes. Diabetic retinopathy (DR) is a microvascular complication that occurs in people with diabetes that causes abnormalities in the retina and can both cause blindness. This study was conducted to build an application that can automatically classify Diabetic Retinopathy in accordance with level, ie normal, non-proliferate diabe...