Proceedings ArticleDOI
Very Fast Decision Tree (VFDT) algorithm on Hadoop
Sharmishta Desai,Sourav Roy,Brina Patel,Samruddhi Purandare,Minal Kucheria +4 more
- Vol. 2016, pp 1-7
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TLDR
This paper has replaced the serial execution of VFDT algorithm by a series of Map and Reduce functions and conducted an extensive analysis on various datasets which have proved the proposed algorithm to be more efficient in terms of time compared to the other existing decision tree models.Abstract:
In the era of Big Data where voluminous data is handled on a very large scale, traditional decision trees might be very time consuming and sometimes might even fail to work owing to its dataset size. Handling Big Data can also be a costly affair because of its high demand for memory and other hardware requirements. To the end of this paper, we have chosen a decision tree algorithm named Very Fast Decision Tree (VFDT) after comparing it with other decision tree algorithms like ID3 and C4.5. We have also proposed an algorithm for implementing VFDT on a Distributed Environment called Hadoop. This implementation can form a base for a large number of applications for handling Big Data. We have replaced the serial execution of VFDT algorithm by a series of Map and Reduce functions. We have also conducted an extensive analysis on various datasets which have proved our proposed algorithm to be more efficient in terms of time compared to the other existing decision tree models.read more
Citations
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A Classification for Patients with Heart Disease Based on Hoeffding Tree
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Book ChapterDOI
Improving Accuracy of Classification Based on C4.5 Decision Tree Algorithm Using Big Data Analytics
Bhavna Rawal,Ruchi Agarwal +1 more
TL;DR: The main objective of this research is to boost up the classification accuracy and roll back timing to build a classification model and has reduced input space using Bhattacharya distance.
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Use of Ensemble Machine Learning to Detect Depression in Social Media Posts
TL;DR: In this article, a system to detect depression using ensembled learning and Natural Language Processing (NLP) techniques was proposed, and the best performing configuration gave an accuracy of 96.35%.
References
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Journal ArticleDOI
A MapReduce Implementation of C4.5 Decision Tree Algorithm
TL;DR: This work proposes to implement a typical decision tree algorithm, C4.5, using MapReduce programming model, and transforms the traditional algorithm into a series of Map and Reduce procedures, showing both time efficiency and scalability.
Proceedings ArticleDOI
Efficient regression algorithms for classification of social media data
TL;DR: This paper has implemented decision tree algorithm and compared it with other machine learning algorithms like NaiveBayes, Adaboost etc and found that decision tree algorithms gives accurate results as compared to other algorithms.
A Comparative study of Stream Data mining Algorithms
TL;DR: A comparative study between Hoeffding tree, VF DT (Very Fast Decision Tree) and CVFDT (Concept Adaptinf Very Fast decision Tree) from various algorithms which are used for stream data classification are made.
Journal ArticleDOI
Efficient classification of big data using vfdt (very fast decision tree)
TL;DR: A comparative study on three of these popular decision tree algorithms - (Iterative Dichotomizer 3), C4.5 which is an evolution of ID3 and VFDT (Very Fast Decision Tree) has been made and various conclusions have been drawn.