{"title":"Comparative performance of backpropagation networks designed by genetic algorithms and heuristics","authors":"J. Hansen","doi":"10.1002/(SICI)1099-1174(199806)7:2%3C69::AID-ISAF143%3E3.0.CO;2-2","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199806)7:2%3C69::AID-ISAF143%3E3.0.CO;2-2","url":null,"abstract":"The design of neural network models involves numerous complexities, including the determination of input vectors, choosing the number of hidden layers and their computational units, and specifying activation functions for the latter. The combinatoric possibilities are daunting, yet experience has yielded informal guidelines that can be useful. Alternatively, current research on genetic algorithms (GA) suggests that they might be of practical use as a formal method of determining ‘good’ architectures for neural networks. In this paper, we use a genetic algorithm to find effective architectures for backpropagation neural networks (BP). We compare the performance of heuristically designed BP networks with that of GA-designed BP networks. Our test domains are sets of problems having compensatory, conjunctive, and mixed-decision structures. The results of our experiment suggest that heuristic methods produce architectures that are simpler and yet perform comparatively well. 1998 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115468005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Refining the behavior of multiple expert systems: a concept and empirical results","authors":"C. Holsapple, Anita Lee, James R. Otto","doi":"10.1002/(SICI)1099-1174(199806)7:2%3C81::AID-ISAF142%3E3.0.CO;2-7","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199806)7:2%3C81::AID-ISAF142%3E3.0.CO;2-7","url":null,"abstract":"","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121874121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neural network detection of management fraud using published financial data","authors":"K. Fanning, K. O. Cogger","doi":"10.1002/(SICI)1099-1174(199803)7:1%3C21::AID-ISAF138%3E3.0.CO;2-K","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199803)7:1%3C21::AID-ISAF138%3E3.0.CO;2-K","url":null,"abstract":"This paper uses Artificial Neural Networks to develop a model for detecting management fraud. Although similar to the more widely investigated area of bankruptcy prediction, research has been minimal. To increase the body of knowledge on this subject, we offer an in-depth examination of important publicly available predictors of fraudulent financial statements. We test the value of these suggested variables for detection of fraudulent financial statements within a matched pairs sample. We use a self organizing Artificial Neural Network (ANN) AutoNet in conjunction with standard statistical tools to investigate the usefulness of these publicly available predictors. Our study results in a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}