| 1. |
- Afzal, Wasif, et al.
(författare)
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On the application of genetic programming for software engineering predictive modeling : A systematic review
- 2011
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Ingår i: Expert Systems with Applications. - : Pergamon-Elsevier Science Ltd. - 0957-4174 .- 1873-6793. ; 38:9, s. 11984-11997
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Forskningsöversikt (refereegranskat)abstract
- The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modeling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling: the results are inconclusive for software cost/effort/size estimation.
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| 2. |
- Argyrou, Argyris, et al.
(författare)
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A semi-supervised tool for clustering accounting databases with applications to internal controls
- 2011
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 38:9, s. 11176-11181
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Tidskriftsartikel (refereegranskat)abstract
- A considerable body of literature attests to the significance of internal controls; however, little is known on how the clustering of accounting databases can function as an internal control procedure. To explore this issue further, this paper puts forward a semi-supervised tool that is based on self-organizing map and the IASB XBRL Taxonomy. The paper validates the proposed tool via a series of experiments on an accounting database provided by a shipping company. Empirical results suggest the tool can cluster accounting databases in homogeneous and well-separated clusters that can be interpreted within an accounting context. Further investigations reveal that the tool can compress a large number of similar transactions, and also provide information comparable to that of financial statements. The findings demonstrate that the tool can be applied to verify the processing of accounting transactions as well as to assess the accuracy of financial statements, and thus supplement internal controls.
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| 3. |
- Bacauskiene, Marija, et al.
(författare)
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Random forests based monitoring of human larynx using questionnaire data
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 5506-5512
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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| 4. |
- Begum, Shahina, et al.
(författare)
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Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
- 2014
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 41:2, s. 295-305
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Tidskriftsartikel (refereegranskat)abstract
- Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis-Case-Based Reasoning (MMSE-CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE-CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify 'stressed' and 'healthy' subjects 83.33% correctly compare to an expert's classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions 'adapt' (training) and 'sharp' (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE-CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.
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| 5. |
- Borg, Anton, et al.
(författare)
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Detecting serial residential burglaries using clustering
- 2014
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Ingår i: Expert Systems with Applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 41:11, s. 5252-5266
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Tidskriftsartikel (refereegranskat)abstract
- According to the Swedish National Council for Crime Prevention, law enforcement agencies solved approximately three to five percent of the reported residential burglaries in 2012. Internationally, studies suggest that a large proportion of crimes are committed by a minority of offenders. Law enforcement agencies, consequently, are required to detect series of crimes, or linked crimes. Comparison of crime reports today is difficult as no systematic or structured way of reporting crimes exists, and no ability to search multiple crime reports exist. This study presents a systematic data collection method for residential burglaries. A decision support system for comparing and analysing residential burglaries is also presented. The decision support system consists of an advanced search tool and a plugin-based analytical framework. In order to find similar crimes, law enforcement officers have to review a large amount of crimes. The potential use of the cut-clustering algorithm to group crimes to reduce the amount of crimes to review for residential burglary analysis based on characteristics is investigated. The characteristics used are modus operandi, residential characteristics, stolen goods, spatial similarity, or temporal similarity. Clustering quality is measured using the modularity index and accuracy is measured using the rand index. The clustering solution with the best quality performance score were residential characteristics, spatial proximity, and modus operandi, suggesting that the choice of which characteristic to use when grouping crimes can positively affect the end result. The results suggest that a high quality clustering solution performs significantly better than a random guesser. In terms of practical significance, the presented clustering approach is capable of reduce the amounts of cases to review while keeping most connected cases. While the approach might miss some connections, it is also capable of suggesting new connections. The results also suggest that while crime series clustering is feasible, further investigation is needed.
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| 6. |
- Englund, Cristofer, et al.
(författare)
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A novel approach to estimate proximity in a random forest : An exploratory study
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier BV. - 0957-4174 .- 1873-6793. ; 39:17, s. 13046-13050
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Tidskriftsartikel (refereegranskat)abstract
- A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel.
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| 7. |
- Georgoulas, George, et al.
(författare)
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Principal component analysis of the start-up transient and hidden Markov modeling for broken rotor bar fault diagnosis in asynchronous machines
- 2013
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 40:17, s. 7024-7033
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMM, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.
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| 8. |
- Gerdes, Mike
(författare)
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Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning
- 2013
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 40:12, s. 5021-5026
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Tidskriftsartikel (refereegranskat)abstract
- Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.
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| 9. |
- Kalsyte, Zivile, et al.
(författare)
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A novel approach to designing an adaptive committee applied to predicting company’s future performance
- 2013
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Ingår i: Expert systems with applications. - Oxford : Pergamon Press. - 0957-4174 .- 1873-6793. ; 40:6, s. 2051-2057
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Tidskriftsartikel (refereegranskat)abstract
- This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company's future performance. Current liabilities/Current assets, Total liabilities/Total assets, Net income/Total assets, and Operating Income/Total liabilities are the attributes used in this paper. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. A random forest is used a basic model in this study. The developed technique was tested on data concerning companies from ten sectors of the healthcare industry of the United States and compared with results obtained from averaging and weighted averaging committees. The proposed adaptivity of a committee size and aggregation weights led to a statistically significant increase in prediction accuracy if compared to other types of committees. © 2012 Elsevier Ltd. All rights reserved.
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| 10. |
- Kalsyte, Zivile, et al.
(författare)
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A novel approach to exploring company’s financial soundness : Investor’s perspective
- 2013
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Ingår i: Expert systems with applications. - Oxford : Pergamon Press. - 0957-4174 .- 1873-6793. ; 40:13, s. 5085-5092
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Tidskriftsartikel (refereegranskat)abstract
- Prediction of company's life cycle stage change; creation of an ordered 2D map allowing to explore company's financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique ''t-distributed stochastic neighbor embedding''. Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company's financial soundness from a rating agency perspective. The proposed use of information not only from companies' financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody's rating agency. Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company's management team. It was also found that company's life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging. © 2013 Elsevier Ltd. All rights reserved.
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