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Comparative Analysis of Industrial Mishaps Based on Classified Prediction


Nahida Parvin, Ayesha Aziz Prova, Mehnaz Tabassum
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Industrial accident analysis is a very challenging task and one of most vital issues in the era of globalization. Discovering the attributes becomes more complex because voluminous factors are associated. We have tried to find the specific attributes and made a cumulative dataset depending on the reliable sources allied to Bangladesh. In the study, we have evaluated a meticulous survey on various classification techniques to achieve casualty for textile & garments accidents. We have presented a comparative analysis of accuracy between base and AdaBoost Meta classifier using base classifiers, such as: OneR, J48, REPTree, SimpleCART & Na´ve Bayes. The analysis unfurl that using ensemble method with the base classifiers improve accuracy level between 1.8%-6.36%. Depending on the knowledge explored by classification technique which will have the ability to make automated decision that is quite similar to human decision making for reducing the rate of casualty of industrial mishaps.

Nahida Parvin, Ayesha Aziz Prova, Mehnaz Tabassum

Data Mining, Classification Algorithms, Meta Classifier, Textile & Garments Accident Data

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Publication Details

Published in : Volume 1 | Issue 6 | November-December - 2015
Date of Publication Print ISSN Online ISSN
2015-12-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
220-226 IJSRSET151632   Technoscience Academy

Cite This Article

Nahida Parvin, Ayesha Aziz Prova, Mehnaz Tabassum , "Comparative Analysis of Industrial Mishaps Based on Classified Prediction ", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 6, pp.220-226, November-December-2015.
URL : http://ijsrset.com/IJSRSET151632.php