Predicting the listing status of Chinese listed companies with multi-class classification models

Zhou, Ligang; Tam, Kwo Ping; Fujita, Hamido

doi:10.1016/j.ins.2015.08.036

Cited by 47 publications

(25 citation statements)

References 23 publications

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“…Thus, while “bankruptcy” covers firms in a legal situation of insolvency, “financial difficulties” usually classifies firms in accordance with solvency ratios established by a reference criterion. For example, [20] perform a multi-class classification of Chinese firms.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

A Global Model for Bankruptcy Prediction

2016

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The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

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Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

A Global Model for Bankruptcy Prediction

2016

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“…In the neural network, the ability and efficiency to solve the problem, to a large extent, depends on the activation function used by the network in addition to the network structure [25]. The choice of the activation function has great influence on the convergence speed of the network.…”

Section: The Activation Functionmentioning

confidence: 99%

Application of artificial neural network in the diagnostic system of osteoporosis

Xiang

2016

Neurocomputing

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“…Third, differing from most prior studies, our proposed models aimed to predict a multi-class classification task, which may provide more accurate predictions over and above binary classification tasking ( Zhou, Tam & Fujita, 2016 ) since there may be numerous features that specifically identify a certain category. It is therefore of practical significance to apply a multi-class classification approach useful to predict ICD-10-CM code for T2DM patients.…”

Section: Discussionmentioning

confidence: 99%

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

Kuo

Talley

Kao

et al. 2020

PeerJ

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Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

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Predicting the listing status of Chinese listed companies with multi-class classification models

Cited by 47 publications

References 23 publications

A Global Model for Bankruptcy Prediction

A Global Model for Bankruptcy Prediction

Application of artificial neural network in the diagnostic system of osteoporosis

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

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