Open AccessImproving Decision Tree Forest using Preprocessed Data
Author(s) -
Archana R. Panhalkar,
Dharmpal D. Doye
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8136.038620
Subject(s) - random forest , decision tree , computer science , data mining , preprocessor , data pre processing , tree (set theory) , artificial intelligence , machine learning , pattern recognition (psychology) , mathematics , mathematical analysis
Random forest is one of the best techniques in data mining for classification. It not only improves accuracy of classification but performing best for various data types. Data mining researchers concentrated on improving random tree forest by constructing trees by using various methods. In this paper, we are improving decision forest by applying various preprocessing techniques. Decision tree forest is created by using bootstrapped samples. Trees created using preprocessed data improves not only accuracy of classification but also improves time required to construct forest. Experiments are carried out on various UCI data sets to show better performance of our proposed system.