Precipitation’s Level Prediction Based on Tree Augmented Naïve Bayes model

Decision Tree Induction 17.1 Introduction 17.2 Attribute selection measure 17.3 Tree Pruning 17.4 Extracting Classification Rules from Decision Trees 17.5 Bayesian Classification 17.6 Bayes Theorem 17.7 Naïve Bayesian Classification 17.8 Bayesian Belief Networks

Augmented Naïve Bayesian Model of Classification Learning

The Naïve Bayesian Classifier and an Augmented Naïve Bayesian Classifier are applied to human classification tasks. The Naïve Bayesian Classifier is augmented with feature construction using a Galois lattice. The best features, measured on their withinand between-category overlap, are added to the category’s concept description. The results show that space efficient concept descriptions can pre...

A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features

The Tree Augmented Naı̈ve Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies. In this work, we propose a Hierarchical Redundancy Eliminated Tree Augmented Naı̈ve Bayes (HRE–TAN) algorithm, which considers removing the hierarchical redundancy during the classifier learning process, when coping with data containing hierarchically structured feat...

Comparison of Decision Tree and Naïve Bayes Methods in Classification of Researcher’s Cognitive Styles in Academic Environment

In today world of internet, it is important to feedback the users based on what they demand. Moreover, one of the important tasks in data mining is classification. Today, there are several classification techniques in order to solve the classification problems like Genetic Algorithm, Decision Tree, Bayesian and others. In this article, it is attempted to classify researchers to “Expert” and “No...

Modified Naïve Bayes Based Prediction Modeling for Crop Yield Prediction

Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally eff...