Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. Naïve Bayes is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. K-means clustering is one of the most popular clustering techniques; h...

Naïve Bayes (NB) is an efficient and effective classifier in many cases. However, NB might suffer from poor performance when its conditional independence assumption is violated. While most recent research focuses on improving NB by alleviating the conditional independence assumption, we propose a new Meta learning technique to scale up NB by assuming an altered strategy to the traditional Casca...

With the proliferation of unstructured data, text classification or text categorization has found many applications in topic classification, sentiment analysis, authorship identification, spam detection, and so on. There are many classification algorithms available. Naïve Bayes remains one of the oldest and most popular classifiers. On one hand, implementation of naïve Bayes is simple and, on t...

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely cont...

This study provides operational guidance for using naïve Bayes Bayesian network (BN) models in bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors from a pool of potential variables. The method is based upon the assumption that the joint distribution of the variables is multivariate normal. Variables are selected based upon correlations...

Classification, particularly Text Classification, is a supervised learning approach categorizing into various categories, the available training set of correctly identified observations analyzed into a set of features. There are many phases involved in classification. The main classification phase involves the use of classification algorithms or classifiers. Among the various classifiers, the N...

Voting methods such as boosting and bagging provide substantial improvements in classification performance in many problem domains. However, the resulting predictions can prove inscrutable to end-users. This is especially problematic in domains such as medicine, where end-user acceptance often depends on the ability of a classifier to explain its reasoning. Here we propose a variant of the boos...

The supervised classification also known as pattern recognition, discrimination, or supervised learning consists of assigning new cases to one of a set of pre-defined classes given a sample of cases for which the true classes are known. The Naïve Bayes (NB) technique of supervised classification has become increasingly popular in the recent years. Despite its unrealistic assumption that feature...

With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...