Learning with con gurable operators and RL-based heuristics

In this paper, we push forward the idea of machine learning systems for which the operators can be modi ed and netuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators a ect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is de ned as a choice of operator and rule. As a result, the architecture can be seen as a `system for writing machine learning systems' or to explore new operators.This work was supported by the MEC projects CONSOLIDER-INGENIO 26706 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, 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 Econom´ıa y Competitividad in Spain. Also, F. Mart´ınez-Plumed is supported by FPI-ME grant BES-2011-045099Martínez Plumed, F.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2013). Learning with con gurable operators and RL-based heuristics. En New Frontiers in Mining Complex Patterns. Springer Verlag (Germany). 7765:1-16. https://doi.org/10.1007/978-3-642-37382-4_1S1167765Armstrong, J.: A history of erlang. In: Proceedings of the Third ACM SIGPLAN Conf. on History of Programming Languages, HOPL III, pp. 1–26. ACM (2007)Brazdil, P., Giraud-Carrier: Metalearning: Concepts and systems. In: Metalearning. Cognitive Technologies, pp. 1–10. Springer, Heidelberg (2009)Daumé III, H., Langford, J.: Search-based structured prediction (2009)Dietterich, T., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008)Dietterich, T.G., Lathrop, R., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)Džeroski, S.: Towards a general framework for data mining. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 259–300. Springer, Heidelberg (2007)Dzeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Machine Learning 43, 7–52 (2001), 10.1023/A:1007694015589Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer (2001)Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Similarity functions for structured data. an application to decision trees. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 10(29), 109–121 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Web categorisation using distance-based decision trees. ENTCS 157(2), 35–40 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Bridging the Gap between Distance and Generalisation. Computational Intelligence (2012)Ferri-Ramírez, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233–247. Springer, Heidelberg (2001)Gärtner, T.: Kernels for Structured Data. PhD thesis, Universitat Bonn (2005)Holland, J.H., Booker, L.B., Colombetti, M., Dorigo, M., Goldberg, D.E., Forrest, S., Riolo, R.L., Smith, R.E., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: What is a learning classifier system? In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 3–32. Springer, Heidelberg (2000)Holmes, J.H., Lanzi, P., Stolzmann, W.: Learning classifier systems: New models, successful applications. Information Processing Letters (2002)Kitzelmann, E.: Inductive programming: A survey of program synthesis techniques. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 50–73. Springer, Heidelberg (2010)Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco (1997)Lafferty, J., McCallum, A.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001, pp. 282–289 (2001)Lloyd, J.W.: Knowledge representation, computation, and learning in higher-order logic (2001)Maes, F., Denoyer, L., Gallinari, P.: Structured prediction with reinforcement learning. Machine Learning Journal 77(2-3), 271–301 (2009)Martínez-Plumed, F., Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Newton trees. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 174–183. Springer, Heidelberg (2010)Muggleton, S.: Inverse entailment and Progol. New Generation Computing (1995)Muggleton, S.H.: Inductive logic programming: Issues, results, and the challenge of learning language in logic. Artificial Intelligence 114(1-2), 283–296 (1999)Plotkin, G.: A note on inductive generalization. Machine Intelligence 5 (1970)Schmidhuber, J.: Optimal ordered problem solver. Maching Learning 54(3), 211–254 (2004)Srinivasan, A.: The Aleph Manual (2004)Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)Tadepalli, P., Givan, R., Driessens, K.: Relational reinforcement learning: An overview. In: Proc. of the Workshop on Relational Reinforcement Learning (2004)Tamaddoni-Nezhad, A., Muggleton, S.: A genetic algorithms approach to ILP. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 285–300. Springer, Heidelberg (2003)Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)Wallace, C.S., Dowe, D.L.: Refinements of MDL and MML coding. Comput. J. 42(4), 330–337 (1999)Watkins, C., Dayan, P.: Q-learning. Machine Learning 8, 279–292 (1992

A computational analysis of general intelligence tests for evaluating cognitive development

[EN] The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the fluid or crystallised intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven s Progressive Matrices and Thurstone s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive perational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2015-69175-C4-1-R and TIN 2013-45732-C4-1-P, and by Generalitat Valenciana under grant PROMETEOII/2015/013.Martínez-Plumed, F.; Ferri Ramírez, C.; Hernández-Orallo, J.; Ramírez Quintana, MJ. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. Cognitive Systems Research. 43:100-118. https://doi.org/10.1016/j.cogsys.2017.01.006S1001184

Aggregative quantification for regression

The final publication is available at Springer via http://dx.doi.org/10.1007/s10618-013-0308-zThe 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.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.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. https://doi.org/10.1007/s10618-013-0308-zS475518282Alonzo TA, Pepe MS, Lumley T (2003) Estimating disease prevalence in two-phase studies. Biostatistics 4(2):313–326Anderson T (1962) On the distribution of the two-sample Cramer–von Mises criterion. Ann Math Stat 33(3):1148–1159Bakar 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 132–140Bella 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, HersheyBella 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 341–349Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2010) Quantification via probability estimators. In: International conference on data mining, ICDM2010, pp 737–742Bella A, Ferri C, Hernández-Orallo J, Ramírez-Quintana MJ (2012) On the effect of calibration in classifier combination. Appl Intell. doi: 10.1007/s10489-012-0388-2Chan 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 89–96Chawla N, Japkowicz N, Kotcz A (2004) Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor Newsl 6(1):1–6Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30Dougherty 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 194–202Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1):27–38Flach P (2012) Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press, CambridgeForman G (2005) Counting positives accurately despite inaccurate classification. In: Proceedings of the 16th European conference on machine learning (ECML), pp 564–575Forman 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 157–166Forman G (2008) Quantifying counts and costs via classification. Data Min Knowl Discov 17(2):164–206Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/mlGonzález-Castro V, Alaiz-Rodríguez R, Alegre E (2012) Class distribution estimation based on the Hellinger distance. Inf Sci 218(1):146–164Hastie TJ, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, BerlinHernández-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) 13:2813–2869Hodges J, Lehmann E (1963) Estimates of location based on rank tests. Ann Math Stat 34(5):598–611Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New YorkHwang JN, Lay SR, Lippman A (1994) Nonparametric multivariate density estimation: a comparative study. IEEE Trans Signal Process 42(10):2795–2810Hyndman RJ, Bashtannyk DM, Grunwald GK (1996) Estimating and visualizing conditional densities. J Comput Graph Stat 5(4):315–336Moreno-Torres J, Raeder T, Alaiz-Rodríguez R, Chawla N, Herrera F (2012) A unifying view on dataset shift in classification. Pattern Recogn 45(1):521–530Neyman J (1938) Contribution to the theory of sampling human populations. J Am Stat Assoc 33(201):101–116Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. MIT Press, Cambridge, pp 61–74Raeder T, Forman G, Chawla N (2012) Learning from imbalanced data: evaluation matters. Data Min 23:315–331Sánchez 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 827–836Sturges H (1926) The choice of a class interval. J Am Stat Assoc 21(153):65–66Team R et al (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaTenenbein A (1970) A double sampling scheme for estimating from binomial data with misclassifications. 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PhD thesis, Monash UniversityZadrozny 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 609–616Zadrozny 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 694–69

Can language models automate data wrangling?

[EN] The automation of data science and other data manipulation processes depend on the integration and formatting of 'messy' data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because (1) users expect to solve them with short cues or few examples, and (2) the problems depend heavily on domain knowledge. Interestingly, large language models today (1) can infer from very few examples or even a short clue in natural language, and (2) can integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different variants of the language model Generative Pre-trained Transformer (GPT) to five batteries covering a wide range of data wrangling problems. We compare the effect of prompts and few-shot regimes on their results and how they compare with specialised data wrangling systems and other tools. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks. We provide some guidelines about how they can be integrated into data processing pipelines, provided the users can take advantage of their flexibility and the diversity of tasks to be addressed. However, reliability is still an important issue to overcome.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was funded by the Future of Life Institute, FLI, under grant RFP2-152, the MIT-Spain - INDITEX Sustainability Seed Fund under project COST-OMIZE, the EU (FEDER) and Spanish MINECO under RTI2018-094403-B-C32 and PID2021-122830OB-C42, Generalitat Valenciana under PROMETEO/2019/098 and INNEST/2021/317, EU's Horizon 2020 research and innovation programme under grant agreement No. 952215 (TAILOR) and US DARPA HR00112120007 ReCOG-AI. AcknowledgementsWe thank Lidia Contreras for her help with the Data Wrangling Dataset Repository. We thank the anonymous reviewers from ECMLPKDD Workshop on Automating Data Science (ADS2021) and the anonymous reviewers of this special issue for their comments.Jaimovitch-López, G.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2023). Can language models automate data wrangling?. Machine Learning. 112(6):2053-2082. https://doi.org/10.1007/s10994-022-06259-920532082112

CASP-DM: Context Aware Standard Process for Data Mining

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs

Can language models automate data wrangling?

[ES] La automatización de la ciencia de datos y otros procesos de manipulación de datos dependen de la integración y el formateo de los datos "desordenados". La manipulación de datos es un término que engloba estas tareas tediosas y que requieren mucho tiempo. Tareas como la transformación de fechas, unidades o nombres expresados en diferentes formatos han sido un reto para el aprendizaje automático porque los usuarios esperan resolverlas con pistas cortas o pocos ejemplos, y los problemas dependen en gran medida del conocimiento del dominio. Curiosamente, los grandes modelos lingüísticos de hoy en día infieren a partir de muy pocos ejemplos o incluso de una breve pista en lenguaje natural, e integran grandes cantidades de conocimiento del dominio. Por tanto, es una cuestión de investigación importante analizar si los modelos de lenguaje son un enfoque prometedor para la gestión de datos, especialmente porque sus capacidades siguen creciendo. En este artículo aplicamos diferentes variantes de modelos lingüísticos de GPT a problemas de gestión de datos, comparando sus resultados con los de herramientas especializadas de gestión de datos, y analizando también las tendencias, variaciones y nuevas posibilidades y riesgos de los modelos lingüísticos en esta tarea. Nuestro principal hallazgo es que parecen ser una herramienta poderosa para una amplia gama de tareas de búsqueda de datos, pero la fiabilidad puede ser un problema importante a superar.[EN] The automation of data science and other data manipulation processes depend on the integration and formatting of ‘messy’ data. Data wran gling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because users expect to solve them with short cues or few examples, and the problems depend heavily on domain knowledge. Interestingly, large language models today infer from very few examples or even a short clue in natural language, and integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different language model variants of GPT to data wrangling problems, comparing their results to specialised data wrangling tools, also analysing the trends, variations and further possibilities and risks of language models in this task. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks, but reliability may be an important issue to overcome.Jaimovitch-López, G.; Ferri, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez-Quintana, MJ. (2021). Can language models automate data wrangling?. http://hdl.handle.net/10251/18502

Agrupamiento conceptual jerárquico basado en distancias

En este trabajo de investigación analizamos la relación existente entre el agrupamiento jerárquico basado en distancia y los conceptos que se pueden inducir por generalización a partir de una jerarquía, mostrando que pueden surgir diversas inconsistencias a partir de incompatibilidades entre las distancias subyacentes y los operadores de generalización empleados. En este contexto, hemos definido un marco teórico genérico en el cual, por un lado, hemos propuesto un nuevo algoritmo de agrupamiento en el que integramos el agrupamiento jerárquico basado en distancias y el agrupamiento conceptual, permitiendo observar en las nuevas jerarquías obtenidas si un elemento ha sido integrado a un grupo por la distancia de enlazado o porque se encuentra cubierto por el concepto asociado al grupo. Por otro lado, hemos definido tres niveles diferentes de consistencia entre los operadores de generalización y las distancias a partir de la similitud existente entre las nuevas jerarquías conseguidas con nuestro algoritmo y las correspondientes obtenidas por el algoritmo tradicional jerárquico. Actualmente, nos encontramos trabajando en el análisis de diversas instanciaciones del marco teórico genérico antes mencionado.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI