Review of random forest classification techniques to resolve data imbalance

• Published in: 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM) pp. 72 - 78
• Main Authors: More, A. S., Rana, Dipti P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2017
• Subjects: Balanced Random Forest; Bibliographies; Decision trees; Dynamic Integration Technique; Noise measurement; Random Forest Classification; Sampling; Sampling methods; Support vector machines; Training; Training data; Weighted Random Forest

In this current age, numerous ranges of real word applications with imbalanced dataset is one of the foremost focal point of researcher's inattention. There is the enormous increment of data generation and imbalance within dataset. Processing and knowledge extraction of huge amount of imbalance...