{"_id":"XTtjhnJQ5dzvZwr84","bibbaseid":"satu-zoynulabedin-khanom-ouenniche-shamimkaiser-applicationoffeatureengineeringwithclassificationtechniquestoenhancecorporatetaxdefaultdetectionperformance-2021","author_short":["Satu, M. S.","Zoynul Abedin, M.","Khanom, S.","Ouenniche, J.","Shamim Kaiser, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Singapore","series":"Advances in Intelligent Systems and Computing","title":"Application of Feature Engineering with Classification Techniques to Enhance Corporate Tax Default Detection Performance","isbn":"978-981-334-673-4","doi":"10.1007/978-981-33-4673-4_5","abstract":"The objective of this work is to propose a methodology that is helpful in analyzing tax data and predict significant features that cause tax defaulting. In this work, we gathered a Finnish tax default data of different firms and then split it according to primary and transformed feature sets. Different feature selection techniques were used to explore significant feature sets. After that, we applied various classification techniques into primary and transformed data sets and analyzed experimental outcomes. Besides, almost all classification techniques are represented the highest results for correlation-based feature selection subset evaluation, information gain feature selection and gain ratio attribute evaluation techniques. But, information gain feature selection is found as the most reliable feature selection method in this work. This analysis can be useful as a complementary tool to assess tax default factors in corporate sectors.","language":"en","booktitle":"Proceedings of International Conference on Trends in Computational and Cognitive Engineering","publisher":"Springer","author":[{"propositions":[],"lastnames":["Satu"],"firstnames":["Md.","Shahriare"],"suffixes":[]},{"propositions":[],"lastnames":["Zoynul","Abedin"],"firstnames":["Mohammad"],"suffixes":[]},{"propositions":[],"lastnames":["Khanom"],"firstnames":["Shoma"],"suffixes":[]},{"propositions":[],"lastnames":["Ouenniche"],"firstnames":["Jamal"],"suffixes":[]},{"propositions":[],"lastnames":["Shamim","Kaiser"],"firstnames":["M."],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Kaiser"],"firstnames":["M.","Shamim"],"suffixes":[]},{"propositions":[],"lastnames":["Bandyopadhyay"],"firstnames":["Anirban"],"suffixes":[]},{"propositions":[],"lastnames":["Mahmud"],"firstnames":["Mufti"],"suffixes":[]},{"propositions":[],"lastnames":["Ray"],"firstnames":["Kanad"],"suffixes":[]}],"year":"2021","keywords":"Classification techniques, Feature selection, Tax default detection","pages":"53–63","bibtex":"@inproceedings{satu_application_2021,\n\taddress = {Singapore},\n\tseries = {Advances in {Intelligent} {Systems} and {Computing}},\n\ttitle = {Application of {Feature} {Engineering} with {Classification} {Techniques} to {Enhance} {Corporate} {Tax} {Default} {Detection} {Performance}},\n\tisbn = {978-981-334-673-4},\n\tdoi = {10.1007/978-981-33-4673-4_5},\n\tabstract = {The objective of this work is to propose a methodology that is helpful in analyzing tax data and predict significant features that cause tax defaulting. In this work, we gathered a Finnish tax default data of different firms and then split it according to primary and transformed feature sets. Different feature selection techniques were used to explore significant feature sets. After that, we applied various classification techniques into primary and transformed data sets and analyzed experimental outcomes. Besides, almost all classification techniques are represented the highest results for correlation-based feature selection subset evaluation, information gain feature selection and gain ratio attribute evaluation techniques. But, information gain feature selection is found as the most reliable feature selection method in this work. This analysis can be useful as a complementary tool to assess tax default factors in corporate sectors.},\n\tlanguage = {en},\n\tbooktitle = {Proceedings of {International} {Conference} on {Trends} in {Computational} and {Cognitive} {Engineering}},\n\tpublisher = {Springer},\n\tauthor = {Satu, Md. Shahriare and Zoynul Abedin, Mohammad and Khanom, Shoma and Ouenniche, Jamal and Shamim Kaiser, M.},\n\teditor = {Kaiser, M. Shamim and Bandyopadhyay, Anirban and Mahmud, Mufti and Ray, Kanad},\n\tyear = {2021},\n\tkeywords = {Classification techniques, Feature selection, Tax default detection},\n\tpages = {53--63},\n}\n\n","author_short":["Satu, M. S.","Zoynul Abedin, M.","Khanom, S.","Ouenniche, J.","Shamim Kaiser, M."],"editor_short":["Kaiser, M. S.","Bandyopadhyay, A.","Mahmud, M.","Ray, K."],"key":"satu_application_2021","id":"satu_application_2021","bibbaseid":"satu-zoynulabedin-khanom-ouenniche-shamimkaiser-applicationoffeatureengineeringwithclassificationtechniquestoenhancecorporatetaxdefaultdetectionperformance-2021","role":"author","urls":{},"keyword":["Classification techniques","Feature selection","Tax default detection"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/3831970/collections/RXZ8CCMC/items?key=yHbP3XPqy9y98HsqzqvTRBsG&format=bibtex&limit=100","dataSources":["yda423vBhGBN7Q3WB","eBFLpkHdRMDh9Juvn","M32K9b97FBA3ABT6f","XxnyXEGfAxbBCrK23","vA5ZqqGu3JauixXfw","3aAmSx3tgfhWcb5kG","3qYGLegypxxYXJRSz","yRJWSkg352NXrujhP"],"keywords":["classification techniques","feature selection","tax default detection"],"search_terms":["application","feature","engineering","classification","techniques","enhance","corporate","tax","default","detection","performance","satu","zoynul abedin","khanom","ouenniche","shamim kaiser"],"title":"Application of Feature Engineering with Classification Techniques to Enhance Corporate Tax Default Detection Performance","year":2021}