Multiclass Corporate Failure Prediction by Adaboost.M1

Alfaro, Esteban; Martínez, Matías; Rubio, Noelia García

doi:10.1007/s11294-007-9090-2

Cited by 45 publications

(5 citation statements)

References 13 publications

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“…In this paper, the classical single classifiers and ensemble classifiers in Weka are selected and compared. The single classifiers include J48 [48], NaiveBayes [49], LibSVM [50], and the ensemble classifiers include Bagging [51], AdaBoostM1 [52], and RandomForest [53].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm

Tan

Huang

et al. 2019

Sensors

121

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With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm

Tan

Huang

et al. 2019

Sensors

121

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“…As a result, the class with the highest weighted vote receives the assignment. In particular [52,53],…”

Section: Classificationmentioning

confidence: 99%

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques

Eyupoglu,

Karakuş

2024

JCM

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Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features—age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality—and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.

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“…However, they suppose that an ensemble model with a manifold learning algorithm and KFSOM is good at estimating failure for Chinese listed companies. Alfaro Cortes et al [60] claim that risk management aims to identify key indicators for corporate success. They think that the prediction model generated by boosting techniques improves the accuracy of the classification tree.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Pavličko¹,

Ďurica²,

Mazanec³

2021

Mathematics

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The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

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Multiclass Corporate Failure Prediction by Adaboost.M1

Cited by 45 publications

References 13 publications

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

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