Browsing by Author "Rahman, Md. Mahbobur"

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    Comparison of Different Machine Learning Algorithms for Detecting Bankruptcy
    (2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE, 2021-01) Keya, Maria Sultana; Akter, Himu; Rahman, Md. Atiqur; Rahman, Md. Mahbobur; Emon, Minhaz Uddin; Zulfiker, Md. Sabab
    There has been severe experiments from academics and merchandisers concerning models for Predicting bankruptcy. The paper propounds an extensive rethink of work done during 5 years in the petition of intellectual strategy to accomplish bankruptcy prediction problems. Several machine learning directions are being used in this research paper for Predicting bankruptcy. Some algorithms: AdaBoost, Decision tree, J48, Bagging, Random Forest are used in this paper. By traditional models, machine learning models offer enhancing bankruptcy prediction accuracy. Different types of models are tested using several evaluation metrics. The five years Bagging accuracy range is 95% within 97% among another model. Here include kfold cross-validation(k=10) to measure our accuracy. Bagging accuracy is high in this paper. Confusion matrix is used to recount the perfection of a classification model that gives true values for knowing.
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    Comparison of Different Machine Learning Algorithms for Detecting Bankruptcy
    (2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE, 2021-02-26) Keya, Maria Sultana; Akter, Himu; Rahman, Md. Atiqur; Rahman, Md. Mahbobur; Emon, Minhaz Uddin
    There has been severe experiments from academics and merchandisers concerning models for Predicting bankruptcy. The paper propounds an extensive rethink of work done during 5 years in the petition of intellectual strategy to accomplish bankruptcy prediction problems. Several machine learning directions are being used in this research paper for Predicting bankruptcy. Some algorithms: AdaBoost, Decision tree, J48, Bagging, Random Forest are used in this paper. By traditional models, machine learning models offer enhancing bankruptcy prediction accuracy. Different types of models are tested using several evaluation metrics. The five years Bagging accuracy range is 95% within 97% among another model. Here include kfold cross-validation (k=10) to measure our accuracy. Bagging accuracy is high in this paper. Confusion matrix is used to recount the perfection of a classification model that gives true values for knowing.