A Variable Impacts Measurement in Random Forest for Mobile Cloud Computing

Recently, the importance of mobile cloud computing has increased. Mobile devices can collect personal data from various sensors within a shorter period of time and sensor-based data consists of valuable information from users. Advanced computation power and data analysis technology based on cloud co...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2017-01, Vol.2017 (2017), p.1-13
Main Authors: Hur, Jae-Hee, Park, Young-Ho, Ihm, Sun-Young
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Big Data
Bioinformatics
Cancer
Classification
Cloud computing
Data analysis
Data mining
Decision trees
Electronic devices
Game theory
Impact analysis
Mathematical models
Mobile computing
Regression analysis
Technology assessment
Variables
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Summary:Recently, the importance of mobile cloud computing has increased. Mobile devices can collect personal data from various sensors within a shorter period of time and sensor-based data consists of valuable information from users. Advanced computation power and data analysis technology based on cloud computing provide an opportunity to classify massive sensor data into given labels. Random forest algorithm is known as black box model which is hardly able to interpret the hidden process inside. In this paper, we propose a method that analyzes the variable impact in random forest algorithm to clarify which variable affects classification accuracy the most. We apply Shapley Value with random forest to analyze the variable impact. Under the assumption that every variable cooperates as players in the cooperative game situation, Shapley Value fairly distributes the payoff of variables. Our proposed method calculates the relative contributions of the variables within its classification process. In this paper, we analyze the influence of variables and list the priority of variables that affect classification accuracy result. Our proposed method proves its suitability for data interpretation in black box model like a random forest so that the algorithm is applicable in mobile cloud computing environment.
ISSN:1530-8669
1530-8677
DOI:10.1155/2017/6817627