{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:20:49Z","timestamp":1768879249898,"version":"3.49.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031221361","type":"print"},{"value":"9783031221378","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-22137-8_14","type":"book-chapter","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T04:36:29Z","timestamp":1669178189000},"page":"187-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["On Reducing the\u00a0Bias of\u00a0Random Forest"],"prefix":"10.1007","author":[{"given":"Md. Nasim","family":"Adnan","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"14_CR1","unstructured":"Big Data Stats for the Big Future Ahead. https:\/\/hostingtribunal.com\/blog\/big-data-stats\/"},{"issue":"3","key":"14_CR2","first-page":"37","volume":"17","author":"U Fayyad","year":"1996","unstructured":"Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37\u201353 (1996)","journal-title":"AI Mag."},{"key":"14_CR3","unstructured":"Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, vol. 12. Pearson Education (2011)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group (2017)","DOI":"10.1201\/9781315139470"},{"issue":"1","key":"14_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81\u2013106 (1986)","journal-title":"Mach. Learn."},{"key":"14_CR6","volume-title":"Pattern Recognition and Machine Learning","author":"N Abramson","year":"1963","unstructured":"Abramson, N., Braverman, D., Sebestyen, G.: Pattern Recognition and Machine Learning, vol. 9. Springer, Heidelberg (1963)"},{"issue":"3","key":"14_CR7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/2.485891","volume":"29","author":"AK Jain","year":"1996","unstructured":"Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31\u201344 (1996)","journal-title":"Computer"},{"issue":"4","key":"14_CR8","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/5326.897072","volume":"30","author":"GP Zhang","year":"2000","unstructured":"Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 30(4), 451\u2013462 (2000)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"issue":"2","key":"14_CR9","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJC Burges","year":"1998","unstructured":"Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121\u2013167 (1998)","journal-title":"Data Min. Knowl. Discov."},{"issue":"4","key":"14_CR10","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1009744630224","volume":"2","author":"SK Murthy","year":"1998","unstructured":"Murthy, S.K.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min. Knowl. Disc. 2(4), 345\u2013389 (1998)","journal-title":"Data Min. Knowl. Disc."},{"key":"14_CR11","unstructured":"Quinlan, J.R.: C4.5 - Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1613\/jair.279","volume":"4","author":"JR Quinlan","year":"1996","unstructured":"Quinlan, J.R.: Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4, 77\u201390 (1996)","journal-title":"J. Artif. Intell. Res."},{"key":"14_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/BFb0014141","volume-title":"Advances in Database Technology \u2014 EDBT \u201996","author":"M Mehta","year":"1996","unstructured":"Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: a fast scalable classifier for data mining. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 18\u201332. Springer, Heidelberg (1996). https:\/\/doi.org\/10.1007\/BFb0014141"},{"key":"14_CR14","unstructured":"Srivastava, A., Singh, V., Han, E.-H., Kumar, V.: An Efficient, Scalable, Parallel Classifier for Data Mining, pp. 544\u2013555 (1996). http:\/\/www.Cs.Umn.Edu\/~Kumar\/Papers.Html"},{"key":"14_CR15","unstructured":"Adnan, Md.N., Islam, Md.Z.: ComboSplit: combining various splitting criteria for building a single decision tree. In: International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT, pp. 1\u20138 (2014)"},{"key":"14_CR16","unstructured":"Adnan, Md.N.: Decision tree and decision forest algorithms: on improving accuracy, efficiency and knowledge discovery. Ph.D. thesis, School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia (2017)"},{"key":"14_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-030-05090-0_7","volume-title":"Advanced Data Mining and Applications","author":"MdN Adnan","year":"2018","unstructured":"Adnan, Md.N., Islam, Md.Z., Akbar, Md.M.: On improving the prediction accuracy of a decision tree using genetic algorithm. In: Gan, G., Li, B., Li, X., Wang, S. (eds.) ADMA 2018. LNCS (LNAI), vol. 11323, pp. 80\u201394. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-05090-0_7"},{"key":"14_CR18","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-3-662-45652-1_23","volume-title":"Machine Learning and Cybernetics","author":"MdN Adnan","year":"2014","unstructured":"Adnan, Md.N., Islam, Md.Z., Kwan, P.W.H.: Extended space decision tree. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds.) ICMLC 2014. CCIS, vol. 481, pp. 219\u2013230. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45652-1_23"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Adnan, Md.N., Islam, Md.Z.: A comprehensive method for attribute space extension for Random Forest. In: 2014 17th International Conference on Computer and Information Technology, ICCIT 2014, pp. 25\u201329 (2003)","DOI":"10.1109\/ICCITechn.2014.7073129"},{"key":"14_CR20","unstructured":"Adnan, Md.N., Islam, Md.Z.: Complement random forest. In: Conferences in Research and Practice in Information Technology Series, vol. 168, pp. 89\u201397 (2015)"},{"key":"14_CR21","unstructured":"Adnan, Md.N., Islam, Md.Z.: Improving the random forest algorithm by randomly varying the size of the bootstrap samples for low dimensional data sets. In: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings, pp. 391\u2013396 (2015)"},{"issue":"3","key":"14_CR22","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21\u201344 (2006)","journal-title":"IEEE Circuits Syst. Mag."},{"key":"14_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-3-319-69179-4_21","volume-title":"Advanced Data Mining and Applications","author":"MdN Adnan","year":"2017","unstructured":"Adnan, Md.N., Islam, Md.Z.: Effects of dynamic subspacing in random forest. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 303\u2013312. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-69179-4_21"},{"key":"14_CR24","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.knosys.2016.07.016","volume":"110","author":"MdN Adnan","year":"2016","unstructured":"Adnan, Md.N., Islam, Md.Z.: Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm. Knowl.-Based Syst. 110, 86\u201397 (2016)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"14_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006)","journal-title":"Mach. Learn."},{"issue":"1","key":"14_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"2","key":"14_CR27","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"8","key":"14_CR28","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832\u2013844 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR29","unstructured":"Han, J., Kamber, M., Pei, J.: Concepts and Techniques: Data Mining. Morgan Kaufmann Publishers (2012)"},{"key":"14_CR30","unstructured":"Adnan, Md.N., Islam, Md.Z.: One-vs-all binarization technique in the context of random forest. In: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings, pp. 385\u2013390 (2015)"},{"key":"14_CR31","unstructured":"Lichman, M.: UCI Machine Learning Repository (2013). http:\/\/archive.ics.uci.edu\/ml. http:\/\/archive.ics.uci.edu\/ml\/datasets.html"},{"key":"14_CR32","first-page":"1","volume":"21","author":"MdN Adnan","year":"2017","unstructured":"Adnan, Md.N., Islam, Md.Z.: ForEx++: a new framework for knowledge discovery from decision forests. Australas. J. Inf. Syst. 21, 1\u201320 (2017)","journal-title":"Australas. J. Inf. Syst."},{"key":"14_CR33","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1214\/09-SS054","volume":"4","author":"S Arlot","year":"2010","unstructured":"Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40\u201379 (2010)","journal-title":"Stat. Surv."},{"key":"14_CR34","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/978-3-319-31753-3_25","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"MdN Adnan","year":"2016","unstructured":"Adnan, Md.N., Islam, Md.Z.: Forest CERN: a new decision forest building technique. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9651, pp. 304\u2013315. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-31753-3_25"},{"key":"14_CR35","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.eswa.2017.08.002","volume":"89","author":"MdN Adnan","year":"2017","unstructured":"Adnan, Md.N., Islam, Md.Z.: Forest PA: constructing a decision forest by penalizing attributes used in previous trees. Expert Syst. Appl. 89, 389\u2013403 (2017)","journal-title":"Expert Syst. Appl."},{"key":"14_CR36","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.ins.2021.05.017","volume":"569","author":"MdN Adnan","year":"2021","unstructured":"Adnan, Md.N., Ip, R.H.L., Bewong, M., Islam, Md.Z.: BDF: a new decision forest algorithm. Inf. Sci. 569, 687\u2013705 (2021)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22137-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T08:07:11Z","timestamp":1669190831000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22137-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031221361","9783031221378"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22137-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2022.uqcloud.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"198","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}