Comparative Analysis of WEKA Data Mining Algorithm RandomForest, RandomTree and LADTree for Classification of Indigenous News Data
Sushilkumar R. Kalmegh · 2015
The amount of data in the world and in our lives seems ever-increasing and there's no end to it. We are overwhelmed with data. The WWW overwhelms us with information. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification may refer to categorization, the process in which ideas and objects are recognized, differentiated, and understood. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of RandomForest, RandomTREE, and LADTree classification algorithm. The paper sets out to make comparative evaluation of classifiers RandomForest, RandomTREE, and LADTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTREE is good than RandomForest and LADTree.