A PREDICTION MODEL OF COMPANY HEALTH USING BAGGING CLASSIFIER
Abstract
In business, have many competitions between companies occur to obtain as many profits as possible, Financial Distress is a financial decline that occurs in companies, reflecting the health of the company before bankruptcy started. Therefore, to avoid bankruptcy, it requires a method or tool with high accuracy in identifying company health. This research uses a bagging classifier, which is one type of Ensemble Learning algorithm. To predict financial difficulties, the authors use the bagging classifier algorithm with 0.13% more accurate results than previous studies using the XGBoost algorithm.
Downloads
References
R. K. Brahmana, “Identifying Financial Distress Condition in Indonesia Manufacture Industry.” Birmingham Business School, University of Birmingham, United Kingdom, pp. 1–19, 2007.
H. D. Piatt and M. B. Piatt, “Predicting corporate financial distress: Reflections on choice-based sample bias,” J. Econ. Financ., vol. 26, no. 2, pp. 184–199, 2002.
D. Arditi and T. Pulket, “Predicting the Outcome of Construction Litigation Using an Integrated Artificial Intelligence Model,” J. Comput. Civ. Eng., vol. 24, no. 1, pp. 73–80, Jan. 2010.
M. S. Gameel and K. El-Geziry, “Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network,” Int. J. Econ. Financ., vol. 8, no. Oct, 2016.
B. Krawczyk, L. L. Minku, J. Gama, J. Stefanowski, and M. Woźniak, “Ensemble learning for data stream analysis: A survey,” Inf. Fusion, vol. 37, pp. 132–156, Sep. 2017.
A. Wibowo and A. Purwarianti, “PENERAPAN BAGGING UNTUK MEMPERBAIKI HASIL PREDIKSI NASABAH PERUSAHAAN ASURANSI X,” Kota Batam, 2011.
Y. P. Huang and M. F. Yen, “A new perspective of performance comparison among machine learning algorithms for financial distress prediction,” Appl. Soft Comput. J., vol. 83, p. 105663, Oct. 2019.
C. Sammut and G. Webb, Encyclopedia of Machine Learning and Data Mining, 2nd ed. Boston: Springer US, 2017.
A. Abdelaziz, A. S. Salama, A. M. Riad, and A. N. Mahmoud, “A Machine Learning Model for Predicting of Chronic Kidney Disease Based Internet of Things and Cloud Computing in Smart Cities,” in Security in Smart Cities: Models, Applications, and Challenges , Springer, Cham, 2019, pp. 93–114.
M. Bramer, Principles of Data Mining, Fourth. London: Springer London, 2020.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, Jun. 2014.
M. Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation,” in AAAI Workshop - Technical Report, 2006, vol. WS-06-06, pp. 1015–1021.
Abstract viewed = 410 times
PDF downloaded = 319 times
-a.jpg)
-b.jpg)
