Now showing items 1-9 of 9

    • Active Learning with Clustering for Mining Big Data 

      Ibrahim, Md.; Masud, Salman; Rabby, Reza E (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 ...
    • 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 ...
    • Correlation Based Feature Selection with Clustering for Multi-Class Classification Tasks 

      Alam, Tanvir; Mahfuz, Moumita; Akter, Papiya (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 ...
    • A Feature Group Weighting Method for Classifying High-Dimensional Big Data 

      Sarker, Shakila (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. ...
    • 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 Big Data: A Review 

      Masroor Fattah Bin, Hossain; Abdur Rahman, Mamun; Monika Akter, Mishu (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 ...
    • 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 ...
    • Network Intrusion Classification with Feature Reduction 

      Munir, Md. Sirazul; Alam, Md. Shamsul; Jahan, Irin; Ferdaous, Jannatul (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 ...