{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:10:11Z","timestamp":1750205411687,"version":"3.41.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319575285"},{"type":"electronic","value":"9783319575292"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-57529-2_39","type":"book-chapter","created":{"date-parts":[[2017,4,22]],"date-time":"2017-04-22T12:28:36Z","timestamp":1492864116000},"page":"495-507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-tuning Filers \u2014 Overload Prediction and Preventive Tuning Using Pruned Random Forest"],"prefix":"10.1007","author":[{"given":"Kumar","family":"Dheenadayalan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gopalakrishnan","family":"Srinivasaraghavan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. N.","family":"Muralidhara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,4,23]]},"reference":[{"issue":"1\u20132","key":"39_CR1","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/0004-3702(94)90084-1","volume":"69","author":"H Almuallim","year":"1994","unstructured":"Almuallim, H., Dietterich, T.G.: Learning boolean concepts in the presence of many irrelevant features. Artif. Intell. 69(1\u20132), 279\u2013305 (1994)","journal-title":"Artif. Intell."},{"issue":"1","key":"39_CR2","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."},{"key":"39_CR3","unstructured":"Contributions, M.K.: caret: Classification and Regression Training, r package version 5.15-044 (2012)"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Dheenadayalan, K., Muralidhara, V.N., Datla, P., Srinivasaraghavan, G., Shah, M.: Premonition of storage response class using skyline ranked ensemble method. In: 2014 21st International Conference on High Performance Computing (HiPC), pp. 1\u201310, December 2014","DOI":"10.1109\/HiPC.2014.7116886"},{"key":"39_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/978-3-319-41920-6_41","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"K Dheenadayalan","year":"2016","unstructured":"Dheenadayalan, K., Srinivasaraghavan, G., Muralidhara, V.N.: Pruning a random forest by learning a learning algorithm. MLDM 2016. LNCS (LNAI), vol. 9729, pp. 516\u2013529. Springer, Cham (2016). doi:10.1007\/978-3-319-41920-6_41"},{"key":"39_CR6","unstructured":"Fawagreh, K., Gaber, M.M., Elyan, E.: On extreme pruning of random forest ensembles for real-time predictive applications. CoRR abs\/1503.04996 (2015)"},{"key":"39_CR7","unstructured":"Ganapathi, A.S.: Predicting and Optimizing System Utilization and Performance via Statistical Machine Learning. Ph.D. thesis, EECS Department, University of California, Berkeley, December 2009"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Ganger, G.R., Strunk, J.D., Klosterman, A.J.: Self-*storage: Brick-based storage with automated administration. Technical report, Carnegie Mellon University, School of Computer Science, Technical report (2003)","DOI":"10.21236\/ADA461187"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Ghemawat, S., Gobioff, H., Leung, S.T.: The google file system. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 29\u201343. ACM (2003)","DOI":"10.1145\/1165389.945450"},{"key":"39_CR10","unstructured":"Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359\u2013366. Morgan Kaufmann Publishers Inc. (2000)"},{"key":"39_CR11","unstructured":"Hamerly, G., Elkan, C.: Bayesian approaches to failure prediction for disk drives, pp. 202\u2013209. Morgan Kaufmann Publishers Inc. (2001)"},{"issue":"1\u20132","key":"39_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1\u20132), 273\u2013324 (1997)","journal-title":"Artif. Intell."},{"key":"39_CR13","unstructured":"Lee, E.K.: Performance Modeling and Analysis of Disk Arrays. Ph.D. thesis, EECS Department, University of California, Berkeley, August 1993"},{"issue":"3","key":"39_CR14","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18\u201322 (2002)","journal-title":"R News"},{"issue":"2","key":"39_CR15","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1109\/TPAMI.2008.78","volume":"31","author":"G Martinez-Munoz","year":"2009","unstructured":"Martinez-Munoz, G., Hernandez-Lobato, D., Suarez, A.: An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. Patt. Anal. Mach. Intell. 31(2), 245\u2013259 (2009)","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"39_CR16","first-page":"783","volume":"6","author":"JF Murray","year":"2005","unstructured":"Murray, J.F., Hughes, G.F., Kreutz-Delgado, K.: Machine learning methods for predicting failures in hard drives: a multiple-instance application. J. Mach. Learn. Res. 6, 783\u2013816 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"39_CR17","unstructured":"NetApp Inc.: Managing workload performance by using storage qos. https:\/\/library.netapp.com\/ecmdocs\/ECMP1196798\/html\/GUID-660A6C00-6D7E-4EE5-B97E-9D33C0B706B5.html"},{"key":"39_CR18","unstructured":"Opitz, D.W.: Feature selection for ensembles. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, pp. 379\u2013384. American Association for Artificial Intelligence (1999)"},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Pollack, K.T., Uttamchandani, S.M.: Genesis: a scalable self-evolving performance management framework for storage systems. In: 26th IEEE International Conference on Distributed Computing Systems, p. 33 (2006)","DOI":"10.1109\/ICDCS.2006.43"},{"issue":"1","key":"39_CR20","first-page":"37","volume":"2","author":"DMW Powers","year":"2011","unstructured":"Powers, D.M.W.: Evaluation: from precision, recall and f-measure to roc., informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37\u201363 (2011)","journal-title":"J. Mach. Learn. Technol."},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Schwing, A.G., Zach, C., Zheng, Y., Pollefeys, M.: Adaptive random forest - how many \u201cexperts\u201d to ask before making a decision? In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1377\u20131384. IEEE Computer Society (2011)","DOI":"10.1109\/CVPR.2011.5995684"},{"key":"39_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/3-540-45164-1_41","volume-title":"Machine Learning: ECML 2000","author":"C Tamon","year":"2000","unstructured":"Tamon, C., Xiang, J.: On the boosting pruning problem. In: L\u00f3pez de M\u00e1ntaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 404\u2013412. Springer, Heidelberg (2000). doi:10.1007\/3-540-45164-1_41"},{"key":"39_CR23","unstructured":"Tang, H., Gulbeden, A., Zhou, J., Strathearn, W., Yang, T., Chu, L.: A self-organizing storage cluster for parallel data-intensive applications. In: Proceedings of the 2004 ACM\/IEEE Conference on Supercomputing, p. 52. IEEE Computer Society (2004)"},{"key":"39_CR24","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-03999-7_1","volume-title":"Applications of Supervised and Unsupervised Ensemble Methods","author":"G Tsoumakas","year":"2009","unstructured":"Tsoumakas, G., Partalas, I., Vlahavas, I.: An ensemble pruning primer. In: Okun, O., Valentini, G. (eds.) Applications of Supervised and Unsupervised Ensemble Methods. SCI, vol. 245, pp. 1\u201313. Springer, Heidelberg (2009). doi:10.1007\/978-3-642-03999-7_1"},{"issue":"6","key":"39_CR25","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1109\/TPDS.2007.70788","volume":"19","author":"Y Zhu","year":"2008","unstructured":"Zhu, Y., Jiang, H., Wang, J., Xian, F.: Hba: distributed metadata management for large cluster based storage systems. IEEE Trans. Parallel Distrib. Syst. 19(6), 750\u2013763 (2008)","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-57529-2_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:46:11Z","timestamp":1750203971000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-57529-2_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319575285","9783319575292"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-57529-2_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"23 April 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd2017.snu.ac.kr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}