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Article: Failure prediction of dotcom companies using hybrid intelligent techniques

TitleFailure prediction of dotcom companies using hybrid intelligent techniques
Authors
KeywordsBoosting
Dotcom companies
Ensemble
Failure prediction
Feature selection
Majority voting
t-statistic
Issue Date2009
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa
Citation
Expert Systems With Applications, 2009, v. 36 n. 3 PART 1, p. 4830-4837 How to Cite?
AbstractThis paper presents a novel hybrid intelligent system in the framework of soft computing to predict the failure of dotcom companies. The hybrid intelligent system comprises the techniques such as a Multilayer Perceptrons (MLP), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Classification and Regression Trees (CART). The dataset collected from Wharton Research Data Services (WRDS) consists of 240 dotcom companies (also known as click-and-mortar companies), of which 120 are failed and 120 are healthy. Ten-fold cross validation is performed on the data set for all the techniques considered in their stand-alone mode. Further, two hybrid techniques viz., ensembling and boosting are employed to improve the accuracies. Moreover, t-statistic is performed on the dataset for feature selection purpose and the reduced feature subset with 10 features is extracted. The reduced feature subset is tested with all the techniques and then ensembling and boosting is also done for the reduced feature subset. Results supported by Receiver Operating Characteristic (ROC) curve indicate that the important features extracted by the t-statistic based feature subset selection yielded very high accuracies for all the techniques. Furthermore, the results are superior to those reported in previous studies on the same data set. © 2008 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/129441
ISSN
2015 Impact Factor: 2.981
2015 SCImago Journal Rankings: 1.839
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChandra, DKen_HK
dc.contributor.authorRavi, Ven_HK
dc.contributor.authorBose, Ien_HK
dc.date.accessioned2010-12-23T08:37:19Z-
dc.date.available2010-12-23T08:37:19Z-
dc.date.issued2009en_HK
dc.identifier.citationExpert Systems With Applications, 2009, v. 36 n. 3 PART 1, p. 4830-4837en_HK
dc.identifier.issn0957-4174en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129441-
dc.description.abstractThis paper presents a novel hybrid intelligent system in the framework of soft computing to predict the failure of dotcom companies. The hybrid intelligent system comprises the techniques such as a Multilayer Perceptrons (MLP), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Classification and Regression Trees (CART). The dataset collected from Wharton Research Data Services (WRDS) consists of 240 dotcom companies (also known as click-and-mortar companies), of which 120 are failed and 120 are healthy. Ten-fold cross validation is performed on the data set for all the techniques considered in their stand-alone mode. Further, two hybrid techniques viz., ensembling and boosting are employed to improve the accuracies. Moreover, t-statistic is performed on the dataset for feature selection purpose and the reduced feature subset with 10 features is extracted. The reduced feature subset is tested with all the techniques and then ensembling and boosting is also done for the reduced feature subset. Results supported by Receiver Operating Characteristic (ROC) curve indicate that the important features extracted by the t-statistic based feature subset selection yielded very high accuracies for all the techniques. Furthermore, the results are superior to those reported in previous studies on the same data set. © 2008 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswaen_HK
dc.relation.ispartofExpert Systems with Applicationsen_HK
dc.subjectBoostingen_HK
dc.subjectDotcom companiesen_HK
dc.subjectEnsembleen_HK
dc.subjectFailure predictionen_HK
dc.subjectFeature selectionen_HK
dc.subjectMajority votingen_HK
dc.subjectt-statisticen_HK
dc.titleFailure prediction of dotcom companies using hybrid intelligent techniquesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0957-4174&volume=36&issue=3, part 1&spage=4830&epage=4837&date=2009&atitle=Failure+prediction+of+dotcom+companies+using+hybrid+intelligent+techniques-
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.eswa.2008.05.047en_HK
dc.identifier.scopuseid_2-s2.0-58349098335en_HK
dc.identifier.hkuros177551en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-58349098335&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume36en_HK
dc.identifier.issue3 PART 1en_HK
dc.identifier.spage4830en_HK
dc.identifier.epage4837en_HK
dc.identifier.isiWOS:000263584100081-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChandra, DK=24474058200en_HK
dc.identifier.scopusauthoridRavi, V=15770237000en_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK

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