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A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment

A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment

Saroj Kr Biswas, Debashree Devi, Manomita Chakraborty
Copyright: © 2018 |Volume: 30 |Issue: 4 |Pages: 19
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781522542230|DOI: 10.4018/JOEUC.2018100107
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MLA

Biswas, Saroj Kr, et al. "A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment." JOEUC vol.30, no.4 2018: pp.104-122. http://doi.org/10.4018/JOEUC.2018100107

APA

Biswas, S. K., Devi, D., & Chakraborty, M. (2018). A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment. Journal of Organizational and End User Computing (JOEUC), 30(4), 104-122. http://doi.org/10.4018/JOEUC.2018100107

Chicago

Biswas, Saroj Kr, Debashree Devi, and Manomita Chakraborty. "A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment," Journal of Organizational and End User Computing (JOEUC) 30, no.4: 104-122. http://doi.org/10.4018/JOEUC.2018100107

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Abstract

This article describes how the enormous size of data in IoT needs efficient data mining model for information extraction, classification and mining hidden patterns from data. CBR is a learning, mining and problem-solving approach which solves a problem by relating past similar solved problems. One issue with CBR is feature weight to measure the similarity among cases to mine similar past cases. NN's pruning is a popular method, which extracts feature weights from a trained neural network without losing much generality of the training set by using four mechanisms: sensitivity, activity, saliency and relevance. However, training NN with imbalanced data leads the classifier to get biased towards the majority class. Therefore, this article proposes a hybrid CBR model with RUS and cost sensitive back propagation neural network in IoT environment to deal with the feature weighting problem in imbalance data. The proposed model is validated with six real-life datasets. The experimental results show that the proposed model is better than other feature weighting methods.

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