Data Mining - Concepts and Techniques.

Machine learning models show improved bankruptcy prediction accuracy over traditional models.Various models were tested using different accuracy metrics.Boosting, bagging, and random forest models provide better results. There has been intensive research from academics and practitioners regarding models for predicting bankruptcy and default events, for credit risk management. Seminal academic research has evaluated bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks). In this study, we test machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event, and compare their performance with results from discriminant analysis, logistic regression, and neural networks. We use data from 1985 to 2013 on North American firms, integrating information from the Salomon Center database and Compustat, analysing more than 10,000 firm-year observations. The key insight of the study is a substantial improvement in prediction accuracy using machine learning techniques especially when, in addition to the original Altmans Z-score variables, we include six complementary financial indicators. Based on Carton and Hofer (2006), we use new variables, such as the operating margin, change in return-on-equity, change in price-to-book, and growth measures related to assets, sales, and number of employees, as predictive variables. Machine learning models show, on average, approximately 10% more accuracy in relation to traditional models. Comparing the best models, with all predictive variables, the machine learning technique related to random forest led to 87% accuracy, whereas logistic regression and linear discriminant analysis led to 69% and 50% accuracy, respectively, in the testing sample. We find that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. Our research adds to the discussion of the continuing debate about superiority of computational methods over statistical techniques such as in Tsai, Hsu, and Yen (2014) and Yeh, Chi, and Lin (2014). In particular, for machine learning mechanisms, we do not find SVM to lead to higher accuracy rates than other models. This result contradicts outcomes from Danenas and Garsva (2015) and Cleofas-Sanchez, Garcia, Marques, and Senchez (2016), but corroborates, for instance, Wang, Ma, and Yang (2014), Liang, Lu, Tsai, and Shih (2016), and Cano etal. (2017). Our study supports the applicability of the expert systems by practitioners as in Heo and Yang (2014), Kim, Kang, and Kim (2015) and Xiao, Xiao, and Wang (2016).

Machine learning models and bankruptcy prediction

Dynamic classifier selection

Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naive Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).

A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

Imbalanced enterprise credit evaluation with DTE-SBD

Supervised clustering is employed to generate a number of clusters.Clusters from different classes are pairwise combined to get a number of training subsets.The base classifiers constructed in the training subsets are diverse and locally accurate.Weighted voting mechanism is used to ensemble the base classifiers.The proposed approach improves the accuracy of credit scoring. Credit scoring aims to assess the risk associated with lending to individual consumers. Recently, ensemble classification methodology has become popular in this field. However, most researches utilize random sampling to generate training subsets for constructing the base classifiers. Therefore, their diversity is not guaranteed, which may lead to a degradation of overall classification performance. In this paper, we propose an ensemble classification approach based on supervised clustering for credit scoring. In the proposed approach, supervised clustering is employed to partition the data samples of each class into a number of clusters. Clusters from different classes are then pairwise combined to form a number of training subsets. In each training subset, a specific base classifier is constructed. For a sample whose class label needs to be predicted, the outputs of these base classifiers are combined by weighted voting. The weight associated with a base classifier is determined by its classification performance in the neighborhood of the sample. In the experimental study, two benchmark credit data sets are adopted for performance evaluation, and an industrial case study is conducted. The results show that compared to other ensemble classification methods, the proposed approach is able to generate base classifiers with higher diversity and local accuracy, and improve the accuracy of credit scoring.

Ensemble classification based on supervised clustering for credit scoring

K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in machine learning. Nevertheless, it suffers from some drawbacks such as high memory consumption, low time efficiency, class overlapping and difficulty of setting an appropriate K value. In this study, we propose an Improved K-Nearest Neighbor rule combining Prototype Selection and Local Feature Weighting (IKNN_PSLFW) to address the above issues in one framework. Differing from conventional prototype selection, IKNN_PSLFW not only selects the representative instances as prototypes, but also preserves the information of instances that are not selected. To deal with the class overlapping problem, IKNN_PSLFW explores the feature relevance in local regions by assigning different weights to different features. For an instance with unknown class label, IKNN_PSLFW uses three classification rules corresponding to three scenarios, according to the distance between the instance and each prototype, for classification. To evaluate the performance of IKNN_PSLFW, we conduct experimental study on 20 benchmark datasets. The experimental results show that compared with the conventional KNN rule, some state-of-the-art prototype selection methods and other machine learning algorithms, the proposed IKNN_PSLFW achieves promising classification performance with high time efficiency. 

K-nearest neighbors rule combining prototype selection and local feature weighting for classification

This paper focuses on the risk decision-making problem in customs targeting, whose major responsibility is to inspect the smuggling goods in import/export declarations. In this problem, the estimated smuggling probabilities of import/export goods, which can be obtained by applying statistical analysis to observations (samples), are needed for accurate inspection decision. A critical presumption for statistical analysis is that the samples are homogeneous or subject to certain distributions. Therefore, clustering techniques are usually employed for preprocessing the samples. However, severe heterogeneity and abnormality exist among the large amount of samples and thus hinder the performance of conventional clustering methods for preprocessing. To deal with this problem, a dynamic K-means clustering method is developed in this paper. Through optimizing the validity function that indicates the goodness of clustering result, the entire samples are iteratively divided into a number of clusters. Based on the dynamic K-means clustering method and logistic regression, a risk decision-making approach is proposed and applied to China’s customs targeting. The empirical results show that the proposed approach improves the accuracy and decreases the risk of inspection decision.

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A Risk Decision-making Approach to Customs Targeting

Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule

In this article, we develop a game model consisting of two competitive recyclers and an online retailer. The retailer has the opportunity to recycle second-hand products and resell them to consumers. The recyclers collect second-hand products from consumers and sell them to consumers through their online recycling platforms or the second-hand selling platform of the online retailer. The recyclers incur a fixed cost if they develop their own selling platform. Otherwise, if the recyclers sell products via the online retailer, the recyclers must give a proportion of the sales revenue to the online retailer as a commission. Without retailer encroachment, intuitively, the recyclers will choose to develop their own selling platform if the fixed cost is low. After the online retailer encroaches on the recycling market, recyclers are more willing to sell products through their online recycling platforms. Interestingly, both the online retailer and recyclers could benefit from retailer encroachment. 

Hongshan Xiao

Papers

Ensemble classification based on supervised clustering for credit scoring

K-nearest neighbors rule combining prototype selection and local feature weighting for classification

A Risk Decision-making Approach to Customs Targeting

Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule

Competitive reselling channel choices of recyclers with online retailer encroachment