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GSEL: A Genetic Stacking-Based Ensemble Learning Approach for Incident Classification

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Proceedings of ICETIT 2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 605))

Abstract

A large amount of incident narratives is collected across all industries around the world. These narratives are usually written manually, which might include a significant amount of errors. Due to the size and complexity of such narrative data, advanced text mining and natural language processing (NLP) techniques are essentially required to extract useful information. Automatic document classification is one of such important tasks in NLP. Therefore, to reduce the dimensionality problem in data, the study proposes a genetic stacking-based ensemble learning (GSEL) method using Skip-gram model and Doc2vec framework. The classifiers, namely logistic regression, random forest, k-nearest neighbor, multi-layer perceptron (MLP), and support vector machine are used and their outputs are ensembled to produce better accuracy in prediction. A real-coded genetic algorithm (GA) is used to tune the parameters of ensemble method. Results reveal that the proposed approach is capable of handling a huge amount of text data in analysis and predict with enhanced accuracy as compared to other state-of-the-art algorithms.

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, IIT Kharagpur, Kharagpur, India

    Sobhan Sarkar, Anima Pramanik & J. Maiti

  2. Department of Mechanical Engineering, IIT Kharagpur, Kharagpur, India

    Nikhil Khatedi & A. S. M. Balu

Authors
  1. Sobhan Sarkar
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  2. Anima Pramanik
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  3. Nikhil Khatedi
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  4. A. S. M. Balu
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  5. J. Maiti
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Corresponding author

Correspondence to Sobhan Sarkar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

  2. Department of Electrical Engineering, IIT Delhi, Delhi, Delhi, India

    Bijaya Ketan Panigrahi

  3. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Nagender Kumar Suryadevara

  4. Institute of Information Technology and Management, Delhi, Delhi, India

    Sudhir Kumar Sharma

  5. USICT, GGSIPU, Delhi, Delhi, India

    Amit Prakash Singh

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Sarkar, S., Pramanik, A., Khatedi, N., Balu, A.S.M., Maiti, J. (2020). GSEL: A Genetic Stacking-Based Ensemble Learning Approach for Incident Classification. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_64

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