Now showing items 1-5 of 5
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. ...
Machine Learning for Mining Imbalanced Data
Mining imbalanced data, which is also known as class imbalanced problem is one of the most enormous challenging tasks in machine learning for data mining applications. To achieve overall accurate performance in imbalanced ...
Cluster-Based Under-Sampling with Random Forest for Multi-Class Imbalanced Classification
Multi-class imbalanced classification has emerged as a very challenging re- search area in machine learning for data mining applications. It occurs when the number of training instances representing majority class instances ...
Big Data Mining in the Presence of Concept Drifting
Concept drift in big data mining is an absolute, highly demanding research issue in this digital era. A concept in "concept drift" involved in the field of data mining (DM) and machine learning (ML) studies is referred ...
Correlation Based Feature Selection with Clustering for Multi-Class Classification Tasks
In recent times high dimensional data is increasing rapidly. Reduce the dimensionality has become popular by feature selection process. So many scientists prefer to use correlation base feature selection method for grouping ...