A genetic-based hybrid approach to corporate failure prediction

Lin, Ping-Chen; Chen, Jiah-Shing

doi:10.1504/ijef.2008.017543

Cited by 20 publications

(5 citation statements)

References 35 publications

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“…They concluded that the predictive performance of the H2CBR system is promising and also the most preferred hybrid CBR for short-term bank failure prediction of Chinese listed companies is based on the ranking-order preference function. Other examples of this type of comparisons are done by [248][249][250][251][252][253][254][255][256][257][258][259][260][261][262][263][264][265][266]. Table 18 presents the brief results of these comparisons.…”

Section: Financial Prediction and Planningmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

431

192

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Financial Prediction and Planningmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

431

192

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“…In the future studies, many further aspects improving the method will be considered, in particular: combination of distinct types of classifiers, models' tuning using genetic based optimization [13] and adaptive clustering.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Sequential Values for Debt Recovery

Kajdanowicz

Kazienko

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. The concept of new approach for debt portfolio pattern recognition is presented in the paper. Aggregated prediction of sequential repayment values over time for a set of claims is performed by means of hybrid combination of various machine learning techniques, including clustering of references, model selection and enrichment of input variables with prediction outputs from preceding periods. Experimental studies on real data revealed usefulness of the proposed approach for claim appraisals. The average accuracy was over 93%, much higher than for simplifier methods.

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“…In general, K-means algorithm is a popular method to solve this kind of clustering problem, but the drawback of it is that the accuracy of clustering results needs to be further improved. Therefore, the K-means clustering algorithm is combined genetic algorithms as hybrid genetic models [2], [7] to improve the accuracy of prediction. This study proposes two kinds of EvoDM algorithms as GA-based K-means and MPSO-based K-means which are termed GA-Kmeans and MPSO-Kmeans…”

Section: Evolutionary Data Mining Algorithmmentioning

confidence: 99%

Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction

Liu¹,

Chen²,

Yang³

2012

IJMLC

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This study proposes two kinds of Evolutionary Data Mining (EvoDM) algorithms to the insurance fraud prediction. One is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA). The other is MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO). The dataset used in this study is composed of 6 attributes with 5000 instances for car insurance claim. These 5000 instances are divided into 4000 training data and 1000 test data. Two different initial cluster centers for each attribute are set by means of (a) selecting the centers randomly from the training set and (b) averaging all data of training set, respectively. Thereafter, the proposed GA-Kmeans and MPSO-Kmeans are employed to determine the optimal weights and final cluster centers for attributes, and the accuracy of prediction for test set is computed based on the optimal weights and final cluster centers. Results show that the presented two EvoDM algorithms significantly enhance the accuracy of insurance fraud prediction when compared the results to that of pure K-means algorithm.

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A genetic-based hybrid approach to corporate failure prediction

Cited by 20 publications

References 35 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Prediction of Sequential Values for Debt Recovery

Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction

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