- -

Aggregative quantification for regression

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Aggregative quantification for regression

Show simple item record

Files in this item

dc.contributor.author Bella Sanjuán, Antonio es_ES
dc.contributor.author Ferri Ramírez, César es_ES
dc.contributor.author Hernández Orallo, José es_ES
dc.contributor.author Ramírez Quintana, María José es_ES
dc.date.accessioned 2015-04-27T12:05:54Z
dc.date.available 2015-04-27T12:05:54Z
dc.date.issued 2014-03-01
dc.identifier.issn 1384-5810
dc.identifier.uri http://hdl.handle.net/10251/49300
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/s10618-013-0308-z es_ES
dc.description.abstract The problem of estimating the class distribution (or prevalence) for a new unlabelled dataset (from a possibly different distribution) is a very common problem which has been addressed in one way or another in the past decades. This problem has been recently reconsidered as a new task in data mining, renamed quantification when the estimation is performed as an aggregation (and possible adjustment) of a single-instance supervised model (e.g., a classifier). However, the study of quantification has been limited to classification, while it is clear that this problem also appears, perhaps even more frequently, with other predictive problems, such as regression. In this case, the goal is to determine a distribution or an aggregated indicator of the output variable for a new unlabelled dataset. In this paper, we introduce a comprehensive new taxonomy of quantification tasks, distinguishing between the estimation of the whole distribution and the estimation of some indicators (summary statistics), for both classification and regression. This distinction is especially useful for regression, since predictions are numerical values that can be aggregated in many different ways, as in multi-dimensional hierarchical data warehouses. We focus on aggregative quantification for regression and see that the approaches borrowed from classification do not work. We present several techniques based on segmentation which are able to produce accurate estimations of the expected value and the distribution of the output variable. We show experimentally that these methods especially excel for the relevant scenarios where training and test distributions dramatically differ. es_ES
dc.description.sponsorship We would like to thank the anonymous reviewers for their careful reviews, insightful comments and very useful suggestions. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROME-TEO/2008/051, the COST-European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation MEC/MINECO [CONSOLIDER-INGENIO] es_ES
dc.relation MEC/MINECO [CSD2007-00022] es_ES
dc.relation MEC/MINECO [TIN 2010-21062-C02-02] es_ES
dc.relation GVA [PROMETEO/2008/051] es_ES
dc.relation COST—European Cooperation in the field of Scientific and Technical Research IC0801 AT es_ES
dc.relation REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA) es_ES
dc.relation Ministerio de Economía y Competitividad in Spain es_ES
dc.relation.ispartof Data Mining and Knowledge Discovery es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Quantification es_ES
dc.subject Regression quantification es_ES
dc.subject Probability estimation es_ES
dc.subject Segmentation es_ES
dc.subject Distribution es_ES
dc.subject Aggregation es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Aggregative quantification for regression es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10618-013-0308-z
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Bella Sanjuán, A.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2014). Aggregative quantification for regression. Data Mining and Knowledge Discovery. 28(2):475-518. doi:10.1007/s10618-013-0308-z es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://link.springer.com/article/10.1007%2Fs10618-013-0308-z es_ES
dc.description.upvformatpinicio 475 es_ES
dc.description.upvformatpfin 518 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 28 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 263092
dc.relation.references Alonzo TA, Pepe MS, Lumley T (2003) Estimating disease prevalence in two-phase studies. Biostatistics 4(2):313–326 es_ES
dc.relation.references Anderson T (1962) On the distribution of the two-sample Cramer–von Mises criterion. Ann Math Stat 33(3):1148–1159 es_ES
dc.relation.references Bakar AA, Othman ZA, Shuib NLM (2009) Building a new taxonomy for data discretization techniques. In: Proceedings of 2nd conference on data mining and optimization (DMO’09), pp 132–140 es_ES
dc.relation.references Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2009a) Calibration of machine learning models. In: Handbook of research on machine learning applications. IGI Global, Hershey es_ES
dc.relation.references Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2009b) Similarity-binning averaging: a generalisation of binning calibration. In: International conference on intelligent data engineering and automated learning. LNCS, vol 5788. Springer, Berlin, pp 341–349 es_ES
dc.relation.references Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2010) Quantification via probability estimators. In: International conference on data mining, ICDM2010, pp 737–742 es_ES
dc.relation.references Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2012) On the effect of calibration in classifier combination. Appl Intell. doi: 10.1007/s10489-012-0388-2 es_ES
dc.relation.references Chan Y, Ng H (2006) Estimating class priors in domain adaptation for word sense disambiguation. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp 89–96 es_ES
dc.relation.references Chawla N, Japkowicz N, Kotcz A (2004) Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor Newsl 6(1):1–6 es_ES
dc.relation.references Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30 es_ES
dc.relation.references Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Prieditis A, Russell S (eds) Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 194–202 es_ES
dc.relation.references Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1):27–38 es_ES
dc.relation.references Flach P (2012) Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press, Cambridge es_ES
dc.relation.references Forman G (2005) Counting positives accurately despite inaccurate classification. In: Proceedings of the 16th European conference on machine learning (ECML), pp 564–575 es_ES
dc.relation.references Forman G (2006) Quantifying trends accurately despite classifier error and class imbalance. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 157–166 es_ES
dc.relation.references Forman G (2008) Quantifying counts and costs via classification. Data Min Knowl Discov 17(2):164–206 es_ES
dc.relation.references Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml es_ES
dc.relation.references González-Castro V, Alaiz-Rodríguez R, Alegre E (2012) Class distribution estimation based on the Hellinger distance. Inf Sci 218(1):146–164 es_ES
dc.relation.references Hastie TJ, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin es_ES
dc.relation.references Hernández-Orallo J, Flach P, Ferri C (2012) A unified view of performance metrics: translating threshold choice into expected classification loss. J Mach Learn Res (JMLR) 13:2813–2869 es_ES
dc.relation.references Hodges J, Lehmann E (1963) Estimates of location based on rank tests. Ann Math Stat 34(5):598–611 es_ES
dc.relation.references Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York es_ES
dc.relation.references Hwang JN, Lay SR, Lippman A (1994) Nonparametric multivariate density estimation: a comparative study. IEEE Trans Signal Process 42(10):2795–2810 es_ES
dc.relation.references Hyndman RJ, Bashtannyk DM, Grunwald GK (1996) Estimating and visualizing conditional densities. J Comput Graph Stat 5(4):315–336 es_ES
dc.relation.references Moreno-Torres J, Raeder T, Alaiz-Rodríguez R, Chawla N, Herrera F (2012) A unifying view on dataset shift in classification. Pattern Recogn 45(1):521–530 es_ES
dc.relation.references Neyman J (1938) Contribution to the theory of sampling human populations. J Am Stat Assoc 33(201):101–116 es_ES
dc.relation.references Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. MIT Press, Cambridge, pp 61–74 es_ES
dc.relation.references Raeder T, Forman G, Chawla N (2012) Learning from imbalanced data: evaluation matters. Data Min 23:315–331 es_ES
dc.relation.references Sánchez L, González V, Alegre E, Alaiz R (2008) Classification and quantification based on image analysis for sperm samples with uncertain damaged/intact cell proportions. In: Proceedings of the 5th international conference on image analysis and recognition. LNCS, vol 5112. Springer, Heidelberg, pp 827–836 es_ES
dc.relation.references Sturges H (1926) The choice of a class interval. J Am Stat Assoc 21(153):65–66 es_ES
dc.relation.references Team R et al (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna es_ES
dc.relation.references Tenenbein A (1970) A double sampling scheme for estimating from binomial data with misclassifications. J Am Stat Assoc 65(331):1350–1361 es_ES
dc.relation.references Weiss G (2004) Mining with rarity: a unifying framework. ACM SIGKDD Explor Newsl 6(1):7–19 es_ES
dc.relation.references Weiss G, Provost F (2001) The effect of class distribution on classifier learning: an empirical study. Technical Report ML-TR-44 es_ES
dc.relation.references Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques with Java implementations. Elsevier, Amsterdam es_ES
dc.relation.references Xiao Y, Gordon A, Yakovlev A (2006a) A C++ program for the Cramér–von Mises two-sample test. J Stat Softw 17:1–15 es_ES
dc.relation.references Xiao Y, Gordon A, Yakovlev A (2006b) The L1-version of the Cramér-von Mises test for two-sample comparisons in microarray data analysis. EURASIP J Bioinform Syst Biol 2006:85769 es_ES
dc.relation.references Xue J, Weiss G (2009) Quantification and semi-supervised classification methods for handling changes in class distribution. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 897–906 es_ES
dc.relation.references Yang Y (2003) Discretization for naive-bayes learning. PhD thesis, Monash University es_ES
dc.relation.references Zadrozny B, Elkan C (2001) Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In: Proceedings of the 8th international conference on machine learning (ICML), pp 609–616 es_ES
dc.relation.references Zadrozny B, Elkan C (2002) Transforming classifier scores into accurate multiclass probability estimates. In: The 8th ACM SIGKDD international conference on knowledge discovery and data mining, pp 694–699 es_ES


This item appears in the following Collection(s)

Show simple item record