Now showing items 1-4 of 4

    • Big Data Mining in the Presence of Concept Drifting 

      Siraj, Tabassum; Jannat, Efrana; Rasul, Warafta; Chowdhury, Meher Afroz (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 ...
    • Cluster-Based Under-Sampling with Random Forest for Multi-Class Imbalanced Classification 

      Arafat, Md. Yasir (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 ...
    • Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks 

      Rahman, Chowdhury Mofizur; Farid, Dewan Md; Hossain, M Alamgir; Strachan, Rebecca (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 

      Hoque, Sabera (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 ...