Cost-sensitive AdaBoost Selective Ensemble for Financial Distress Prediction
نویسنده
چکیده
Financial distress prediction (FDP) models are effective tools to prevent stakeholders from suffering economic loss. In the process of FDP, the misclassification cost of typeI error of the model is much higher than that of typeIIerror. Some FPD models based on single classifiers take the asymmetric costs into consideration, but the study on cost-sensitive ensemble approach for FDP is rarely explored. This paper constructs cost-sensitive AdaBoost selective ensemble FDP model for minimizing misclassification cost so that the loss of users of the model will suffer less. On the initial sample of 180 Chinese listed companies and 30 financial ratios, 8 times of holdout experiments are carried out for FDP respectively two years and three years in advance. The experimental results suggest that the proposed approach helps to reduce total misclassification costs compared with FDP model based on cost-sensitive C4.5 decision tree and that based on C4.5 decision tree.
منابع مشابه
ADABOOST ENSEMBLE ALGORITHMS FOR BREAST CANCER CLASSIFICATION
With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...
متن کاملMCELCCh-FDP: Financial distress prediction with classifier ensembles based on firm life cycle and Choquet integral
Financial distress prediction (FDP) has always been an important issue in the business and financial management. This research proposed a novel multiple classifier ensemble model based on firm life cycle and Choquet integral for FDP, named MCELCCh-FDP, as a new approach to tackle with financial distress. Empirical study based on Chinese listed companies’ real data is conducted, and the results ...
متن کاملBankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
The goal of this study is to show an alternative method to corporate failure prediction. In the last decades Artificial Neural Networks have been widely used for this task. These models have the advantage of being able to detect non-linear relationships and show a good performance in presence of noisy information, as it usually happens, in corporate failure prediction problems. AdaBoost is a no...
متن کاملMeasuring Accuracy between Ensemble Methods: AdaBoost.NC vs. SMOTE.ENN
The imbalanced class distribution is one of the main issue in data mining. This problem exists in multi class imbalance, when samples containing in one class are greater or lower than that of other classes. Most existing imbalance learning techniques are only designed and tested for two-class scenarios. The new negative correlation learning (NCL) algorithm for classification ensembles, called A...
متن کاملCUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...
متن کامل