Research Article
Internet of Things: Comparative Study on Classification Algorithms (k-NN, Naive Bayes and Case based Reasoning)
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 130 - Issue 12 |
| Published: November 2015 |
| Authors: Roshna Chettri, Shrijana Pradhan, Lekhika Chettri |
10.5120/ijca2015907120
|
Roshna Chettri, Shrijana Pradhan, Lekhika Chettri . Internet of Things: Comparative Study on Classification Algorithms (k-NN, Naive Bayes and Case based Reasoning). International Journal of Computer Applications. 130, 12 (November 2015), 7-9. DOI=10.5120/ijca2015907120
@article{ 10.5120/ijca2015907120,
author = { Roshna Chettri,Shrijana Pradhan,Lekhika Chettri },
title = { Internet of Things: Comparative Study on Classification Algorithms (k-NN, Naive Bayes and Case based Reasoning) },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 130 },
number = { 12 },
pages = { 7-9 },
doi = { 10.5120/ijca2015907120 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Roshna Chettri
%A Shrijana Pradhan
%A Lekhika Chettri
%T Internet of Things: Comparative Study on Classification Algorithms (k-NN, Naive Bayes and Case based Reasoning)%T
%J International Journal of Computer Applications
%V 130
%N 12
%P 7-9
%R 10.5120/ijca2015907120
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The Internet of Things aims to connect each existing things in the world with internet. It is sweeping all the things to a world like a garland where each flower is connected by a sting forming connectivity. IoT can be considered as a big bucket where everything’s, every data in the world can be poured to form a live-like connectivity, and hence needs data computation for prediction of the unknown data. Data computation in the internet of things is incorporated to return the value data from the huge collected data collected from the different sources device of the IoT. There are various algorithms for computation of data. This paper focus on comparing supervised learning algorithms i.e. K-NN, Naive Bayes and Cased Based Reasoning (CBR) Classifier.The effects of the mentioned algorithms are based on the following parameters i.e. size of the dataset, performance, processing time and accuracy.
References
- Chang, C.L, 1974 Finding Prototypes for Nearest Neighbour Classifiers’ IEEE Trans. On Computers C-23 (11), 1179-1184
- Hardik Maniya Mosin I, Hasan Komal, P. Patel ,2011 Comparative study of Naive Bayes Classifier and KNN for Tuberculosis International Conference on Web Services Computing.
- Janet L. Kolodner , 1992 An Introduction to Case-Based Reasoning’ Artificial Intelligence Review 6, 3--34, Reza Entezari-Maleki
- Tamije Selvy P, Palanisamy V, Elakkiya S , April 2013 Evaluation Of Classification Algorithms For Disease Diagnosis Journal of Global Research in Computer Science. Volume 4, No. 4.
- Website K-NN Algorithm dated 12/10/2015. https://en.wikipedia.org/wiki/K-nearest_neighbors_ algorithm.
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