Design and implementation of classification algorithm in data mining prototype system is described in this paper. This function analyzes a set of training data, constructs a model for each class based on the features in the data, and adjusts the model based on the test data. The architecture of data mining prototype system is defined and the algorithms including ID3,C4.5,SLIQ and Bayesian is di...

In this paper a hybrid adaptive particle swarm optimization aided learnable Bayesian classifier is proposed. The objective of the classifier is to solve some of the fundamental problems associated with the pure naive Bayesian classifier and its variants with a larger view towards maximization of the classifier accuracy. Further, the proposed algorithm can exhibits an improved capability to elim...

The conditional independence assumption of naive Bayes essentially ignores attribute dependencies and is often violated. On the other hand, although a Bayesian network can represent arbitrary attribute dependencies, learning an optimal Bayesian network from data is intractable. The main reason is that learning the optimal structure of a Bayesian network is extremely time consuming. Thus, a Baye...

We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging (BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper’s BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which lea...

We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the behavior of this selective Bayesian network classiier with that of (a) Bayesian network classiiers ...

two different methods of bayesian segmentation algorithm were used with different band combinations. sequential maximum a posteriori (smap) is a bayesian image segmentation algorithm which unlike the traditional maximum likelihood (ml) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately. landsat 7 etm+ data with pa...

Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence ass...

Software bug classification is a precondition for bug fixation and it plays a vital role in software maintenance. It is found that bug fixation often takes long due to the distribution of misclassified or non-classified bugs by the triager among the developers. In this paper, we propose an adaptive bug classification approach on CVE dataset that involves two Bayesian classifiers such as Naive B...

Machine learning provides tools for automatized analysis of data. The most commonly used form of machine learning is supervised classification. A supervised classifier learns a mapping from the descriptive features of an object to the set of possible classes, from a set of features-class pairs. Once learned, it is used to predict the class for novel data instances. The Bayesian network-based su...