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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
(2018-02-19)
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
(2018-02-19)
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
(2019-03-05)
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
(2019-05-20)
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 ...
Network Intrusion Classification with Feature Reduction
(2019-05-29)
Nowadays, in data technology, data preservation has become a good issue. Computers
and completely different security breaches are incessantly attacked by
security threats. There are completely different malicious network ...
Active Learning with Clustering for Mining Big Data
(2019-05-28)
Big data mining is become a key research issue nowadays. It's costly and
also time-consuming to extract knowledge from big data. Big data is so
big, it contains millions of data points that's why it's very difficult to ...
Machine Learning for Mining Big Data: A Review
(2018-11-20)
Development of Big Data is virtually transforming our lifestyle. It is also ac- celerating industrial growth through process optimization, insight discovery and improved decision making. The massive scale of big data exceeds ...
A Feature Group Weighting Method for Classifying High-Dimensional Big Data
(2019-11-25)
Features hold the distinctive characteristics and intrinsic values of data. But it's of
no use if the important information and pattern can not be extracted from the data
coming from disparate sources and applications. ...
Customer Churn Analysis Using Association Rule Mining and Decision Tree Classifiers
(2021-12)
Customer churn is a prominent issue facing companies. Therefore, preventing customer churn and retaining and retaining customers has become an essential issue for business operations and development. This paper aims to ...