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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.<\/jats:p>","DOI":"10.4018\/joeuc.2018100107","type":"journal-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T11:28:42Z","timestamp":1531999722000},"page":"104-122","source":"Crossref","is-referenced-by-count":44,"title":["A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment"],"prefix":"10.4018","volume":"30","author":[{"given":"Saroj Kr","family":"Biswas","sequence":"first","affiliation":[{"name":"NIT Silchar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debashree","family":"Devi","sequence":"additional","affiliation":[{"name":"NIT Silchar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manomita","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"NIT Silchar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"issue":"1","key":"JOEUC.2018100107-0","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3233\/AIC-1994-7104","article-title":"Case-based reasoning: Foundational issues, methodological variations, and system approaches","volume":"7","author":"A.Aamodt","year":"1994","journal-title":"AI Communications"},{"key":"JOEUC.2018100107-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.09.068"},{"key":"JOEUC.2018100107-2","doi-asserted-by":"crossref","unstructured":"Amini, A., Saboohi, H., Wah, T. 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