Failure prediction with self organizing maps

Huysmans, Johan; Baesens, Bart; Vanthienen, Jan; Gestel, Tony Van

doi:10.1016/j.eswa.2005.10.005

Cited by 57 publications

(31 citation statements)

References 15 publications

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“…Chen and Hsiao (2008) as well as Wu et al (2007) used GA to find SVM hyper-parameters. Van Gestel (2006), in contrast, find hyper-parameters for the least squares support vector machine (LS-SVM) by applying the Bayesian evidence framework (MacKay 1992;Gestel et al 2002). Comparison of the efficiency of the GA-and the Bayesian evidence framework-based approaches to determination of the SVM hyper-parameters would be interesting.…”

Section: Genetic Algorithms In Hybrid Techniquesmentioning

confidence: 96%

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Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

et al. 2009

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This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

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Section: Genetic Algorithms In Hybrid Techniquesmentioning

confidence: 96%

“…The obtained map, served as a convenient tool for visual inspection of the analysis results. Huysmans et al (2006) have also combined MLP and SOM, aiming to exploit good data exploration properties of SOM. MLP is trained first using financial input data.…”

Section: Som In Hybrid Systemsmentioning

confidence: 99%

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

et al. 2009

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“…Self Organizing Maps have been used in the condition monitoring area on several occasions. Some works [13,[33][34][35][36] use the map generated with all turbines to explore how the data is distributed by performing an analysis in the unified distance matrix (U-matrix), which is a way to visualize the distances between neurons. Other works go one step further by applying clustering on the U-matrix to find patterns on the map [13,[36][37][38][39].…”

Section: Methodology Overviewmentioning

confidence: 99%

“…According to [13], the initial map size should contain n = 5 √ R neurons, where R is the number of registers and the result is finally rounded up (ceiling). However, [33] indicates that it is possible to obtain the optimal dimensions through an exploratory way by using the U-Matrix [45].…”

Section: Selection Of the Optimal Som Sizementioning

confidence: 99%

Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools

et al. 2018

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Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25-35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results:The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.

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“…Some studies used the visualization capabilities of Self Organized Map (SOM) for exploratory data analysis. Huysmans et al [10] used this method in the first step to offer data analysts an easy way for exploring data. Two data sets from Benelux financial institutions were used in this study.…”

mentioning

confidence: 99%

An ANN Based New Approach Credit Rating Prediction Model: Evidence from Tehran Stock Exchange

Rafiei¹,

Manzari²,

Khashei³

2013

IJCSAI

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Abstract-Corporate credit rating analysis is one of the most important financial problems, which has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods can achieve better performance than traditional statistical methods. In this paper, Multilayer Perceptrons (MLPs) and multiple statistics methods are used to carry out multi-class credit rating of listed corporations in Tehran Stock Exchange (TSE). To accomplish this goal, a sample of these corporations is randomly selected and financial ratios as independent variables are computed for them. Dependent variable is a discrete variable of four groups as the corporation's bond ratings. The sample is split into training set and test set. With training set sample, the model is constructed through Multiple Discriminant Analysis, and with the remaining, hold out sample of corporations, the model is evaluated. Financial ratios for 126 manufacturing companies quoted in TSE have been used. Two models based on artificial neural networks and multiple discriminant analysis (MDA) are utilized to bond rating of corporations. ANN model achieved 81% and 68% accuracy rates in training and holdout samples, respectively. To evaluate the reliability of the model, the data were examined with multivariate discriminate analysis method. MDA reached 65.3% correct classifications of total initial sample. Results indicate that financial characteristics of corporations are good criterion for their bond rating and the same variables as other countries are significant in Iran too.

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Failure prediction with self organizing maps

Cited by 57 publications

References 15 publications

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools

An ANN Based New Approach Credit Rating Prediction Model: Evidence from Tehran Stock Exchange

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