Random forest for big data classification in the internet of things using optimal features

被引：89

作者：

Lakshmanaprabu, S. K. ^{[1
]}

Shankar, K. ^{[2
]}

Ilayaraja, M. ^{[2
]}

Nasir, Abdul Wahid ^{[3
]}

Vijayakumar, V. ^{[4
]}

Chilamkurti, Naveen ^{[5
]}

机构：

[1] BS Abdur Rahman Crescent Inst Sci & Technol, Dept Elect & Instrumentat Engn, Chennai, Tamil Nadu, India

[2] Kalasalingam Acad Res & Educ, Sch Comp, Krishnankoil, India

[3] Bannari Amman Inst Technol, Elect & Instrumentat Engn, Sathyamangalam, Tamilnadu, India

[4] Vellore Inst Technol, Sch Comp Sci & Engn, Chennai, Tamil Nadu, India

[5] La Trobe Univ, Comp Sci & IT, Melbourne, Vic, Australia

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2019年 / 10卷 / 10期

关键词：

Internet of things; Big data; E-health; Map reduce; Random forest classifier; Dragonfly algorithm; Optimization; DATA ANALYTICS; SOCIAL INTERNET; ALGORITHM;

D O I：

10.1007/s13042-018-00916-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The internet of things (IoT) is an internet among things through advanced communication without human's operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods.

引用

页码：2609 / 2618

页数：10

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