Now showing items 1-4 of 4
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
(Expert Systems with Applications, 2014-03-31)
In this paper, we introduce two independent hybrid mining algorithms to improve the classification accuracy rates of decision tree (DT) and naïve Bayes (NB) classifiers for the classification of multi-class problems. ...
Enhanced Classification Accuracy on Naive Bayes Data Mining Models
(International Journal of Computer Applications, 2011-08-01)
A classification paradigm is a data mining framework containing all the concepts extracted from the training dataset to differentiate one class from other classes existed in data. The primary goal of the classification ...
Classification by Clustering (CbC): An Approach of Classifying Big Data based on Similarities
Data classification in supervised learning is the process of classifying data for data mining task that helps to analyses data for decision making. The objective of a classification model is to correctly predict the ...
A Hybrid Approach for Feature Subset Selection to Classify High-Dimensional Data
In this modern era, we need to deal with large dimension of data set every day. Be it on social media, business, medicine or gene expression there are thousands of millions data to be processed everyday. So dealing with ...