Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

This paper is about the implementation of a visual tool for Differential Diagnosis of Erythemato-Squamous Diseases based on the classification algorithms; Nearest Neighbor Classifier (NN), Naive Bayesian Classifier using Normal Distribution (NBC) and Voting Feature Intervals-5 (VFI5). This tool enables the doctors to differentiate six types of ErythematoSquamous Diseases using clinical and hist...

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...

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated 17.5 million people died from cardiovascular disease, while 2008, HF disease represented 31% of patient deaths worldwide. One the new breakthroughs for early diagnosis utilizing data mining techniques. this study, Correlate...

We report on an empirical study of supervised learning algorithms that induce models to resolve the meaning of ambiguous words in text. We find that the Naive Bayesian classifier is as accurate as several more sophisticated methods. This is a surprising result since Naive Bayes makes simplifying assumptions about disambiguation that are not realistic. However, our results correspond to a growin...

This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which s...

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing complete classification algorithm is accurate efficient while also considering costs. proposed method integrates Bayesian handle incremental data. app...

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...

Accurate classification of power load is the premise demand-side response capability evaluation, which great significance for management. Therefore, this study proposes a selection method joint characteristics in time domain and frequency based on whale optimization algorithm. The electrical measurement data six typical equipment are selected to form original identification dataset. Firstly, fe...