Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Probabilistic reframing for cost-sensitive regression

© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Knowledge Discovery from Data (TKDD), VOL. 8, ISS. 4, (October 2014) http://doi.acm.org/10.1145/2641758Common-day applications of predictive models usually involve the full use of the available contextual information. When the operating context changes, one may fine-tune the by-default (incontextual) prediction or may even abstain from predicting a value (a reject). Global reframing solutions, where the same function is applied to adapt the estimated outputs to a new cost context, are possible solutions here. An alternative approach, which has not been studied in a comprehensive way for regression in the knowledge discovery and data mining literature, is the use of a local (e.g., probabilistic) reframing approach, where decisions are made according to the estimated output and a reliability, confidence, or probability estimation. In this article, we advocate for a simple two-parameter (mean and variance) approach, working with a normal conditional probability density. Given the conditional mean produced by any regression technique, we develop lightweight “enrichment” methods that produce good estimates of the conditional variance, which are used by the probabilistic (local) reframing methods. We apply these methods to some very common families of costsensitive problems, such as optimal predictions in (auction) bids, asymmetric loss scenarios, and rejection rules.This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, and TIN 2013-45732-C4-1-P and GVA projects PROMETEO/2008/051 and PROMETEO2011/052. Finally, part of this work was motivated by the REFRAME project (http://www.reframe-d2k.org) granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA) and funded by Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).Hernández Orallo, J. (2014). Probabilistic reframing for cost-sensitive regression. ACM Transactions on Knowledge Discovery from Data. 8(4):1-55. https://doi.org/10.1145/2641758S15584G. Bansal, A. Sinha, and H. Zhao. 2008. Tuning data mining methods for cost-sensitive regression: A study in loan charge-off forecasting. Journal of Management Information System 25, 3 (Dec. 2008), 315--336.A. P. Basu and N. Ebrahimi. 1992. Bayesian approach to life testing and reliability estimation using asymmetric loss function. Journal of Statistical Planning and Inference 29, 1--2 (1992), 21--31.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2010. Quantification via probability estimators. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 737--742.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2013. Aggregative quantification for regression. Data Mining and Knowledge Discovery (2013), 1--44.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2009. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications. IGI Global, 128--146.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2011. Using negotiable features for prescription problems. Computing 91, 2 (2011), 135--168.J. Bi and K. P. Bennett. 2003. Regression error characteristic curves. In Proceedings of the 20th International Conference on Machine Learning (ICML’03).Z. Bosnić and I. Kononenko. 2008. Comparison of approaches for estimating reliability of individual regression predictions. Data & Knowledge Engineering 67, 3 (2008), 504--516.Z. Bosnić and I. Kononenko. 2009. An overview of advances in reliability estimation of individual predictions in machine learning. Intelligent Data Analysis 13, 2 (2009), 385--401.L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth.P. F. Christoffersen and F. X. Diebold. 1996. Further results on forecasting and model selection under asymmetric loss. Journal of Applied Econometrics 11, 5 (1996), 561--571.P. F. Christoffersen and F. X. Diebold. 1997. Optimal prediction under asymmetric loss. Econometric Theory 13 (1997), 808--817.I. Cohen and M. Goldszmidt. 2004. Properties and benefits of calibrated classifiers. Knowledge Discovery in Databases: PKDD 2004 (2004), 125--136.S. Crone. 2002. Training artificial neural networks for time series prediction using asymmetric cost functions. In Proceedings of the 9th International Conference on Neural Information Processing.J. Demšar. 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7 (2006), 1--30.M. Dumas, L. Aldred, G. Governatori, and A. H. M. Ter Hofstede. 2005. Probabilistic automated bidding in multiple auctions. Electronic Commerce Research 5, 1 (2005), 25--49.C. Elkan. 2001. The foundations of cost-sensitive learning. In Proceedings of the 17th International Conference on Artificial Intelligence (’01), Bernhard Nebel (Ed.). San Francisco, CA, 973--978.G. Elliott and A. Timmermann. 2004. Optimal forecast combinations under general loss functions and forecast error distributions. Journal of Econometrics 122, 1 (2004), 47--79.T. Fawcett. 2006a. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861--874.T. Fawcett. 2006b. ROC graphs with instance-varying costs. Pattern Recognition Letters 27, 8 (2006), 882--891.C. Ferri, P. Flach, and J. Hernández-Orallo. 2002. Learning decision trees using the area under the ROC curve. In Proceedings of the International Conference on Machine Learning. 139--146.C. Ferri, P. Flach, and J. Hernández-Orallo. 2003. Improving the AUC of probabilistic estimation trees. In Proceedings of the 14th European Conference on Machine Learning (ECML’03). Springer, 121--132.C. Ferri and J. Hernández-Orallo. 2004. Cautious classifiers. In ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004, J. Hernández-Orallo, C. Ferri, N. Lachiche, and P. A. Flach (Eds.). 27--36.P. Flach. 2012. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press.G. Forman. 2008. Quantifying counts and costs via classification. Data Mining and Knowledge Discovery 17, 2 (2008), 164--206.S. García and F. Herrera. 2008. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. The Journal of Machine Learning Research 9, 2677--2694 (2008), 66.R. Ghani. 2005. Price prediction and insurance for online auctions. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). ACM, New York, NY, 411--418.C. W. J. Granger. 1969. Prediction with a generalized cost of error function. Operational Research (1969), 199--207.C. W. J. Granger. 1999. Outline of forecast theory using generalized cost functions. Spanish Economic Review 1, 2 (1999), 161--173.P. Hall, J. Racine, and Q. Li. 2004. Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association 99, 468 (2004), 1015--1026.P. Hall, R. C. L. Wolff, and Q. Yao. 1999. Methods for estimating a conditional distribution function. Journal of the American Statistical Association (1999), 154--163.T. J. Hastie, R. J. Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.J. Hernández-Orallo. 2013. ROC curves for regression. Pattern Recognition 46, 12 (2013), 3395--3411.J. Hernández-Orallo, P. Flach, and C. Ferri. 2012. A unified view of performance metrics: Translating threshold choice into expected classification loss. Journal of Machine Learning Research 13 (2012), 2813--2869.J. Hernández-Orallo, P. Flach, and C. Ferri. 2013. ROC curves in cost space. Machine Learning 93, 1 (2013), 71--91.J. N. Hwang, S. R. Lay, and A. Lippman. 1994. Nonparametric multivariate density estimation: A comparative study. IEEE Transactions on Signal Processing 42, 10 (1994), 2795--2810.R. J. Hyndman, D. M. Bashtannyk, and G. K. Grunwald. 1996. Estimating and visualizing conditional densities. Journal of Computational and Graphical Statistics (1996), 315--336.N. Japkowicz and M. Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press.M. Jino, B. T. de Abreu, and others. 2010. Machine learning methods and asymmetric cost function to estimate execution effort of software testing. In Proceedings of the 2010 3rd International Conference on Software Testing, Verification and Validation (ICST’10). IEEE, 275--284.B. Kitts and B. Leblanc. 2004. Optimal bidding on keyword auctions. Electronic Markets 14, 3 (2004), 186--201.N. Lachiche and P. Flach. 2003. Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In Proceedings of the International Conference on Machine Learning, Vol. 20-1. 416.H. Papadopoulos. 2008. Inductive conformal prediction: Theory and application to neural networks. Tools in Artificial Intelligence 18 (2008), 315--330.H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman. 2002. Inductive confidence machines for regression. In Machine Learning: ECML 2002, Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.). Lecture Notes in Computer Science, Vol. 2430. Springer, Berlin, 185--194.H. Papadopoulos, V. Vovk, and A. Gammerman. 2011. Regression conformal prediction with nearest neighbours. Journal of Artificial Intelligence Research 40, 1 (2011), 815--840.T. Pietraszek. 2007. On the use of ROC analysis for the optimization of abstaining classifiers. Machine Learning 68, 2 (2007), 137--169.J. C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, Boston, 61--74.F. Provost and P. Domingos. 2003. Tree induction for probability-based ranking. Machine Learning 52, 3 (2003), 199--215.R Team and others. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.R. Ribeiro. 2011. Utility-based Regression. PhD thesis, Department of Computer Science, Faculty of Sciences, University of Porto.M. Rosenblatt. 1969. Conditional probability density and regression estimators. Multivariate Analysis II 25 (1969), 31.S. Rosset, C. Perlich, and B. Zadrozny. 2007. Ranking-based evaluation of regression models. Knowledge and Information Systems 12, 3 (2007), 331--353.R. E. Schapire, P. Stone, D. McAllester, M. L. Littman, and J. A. Csirik. 2002. Modeling auction price uncertainty using boosting-based conditional density estimation. In Proceedings of the International Conference on Machine Learning. 546--553.G. Shafer and V. Vovk. 2008. A tutorial on conformal prediction. Journal of Machine Learning Research 9 (2008), 371--421.J. A. Swets, R. M. Dawes, and J. Monahan. 2000. Better decisions through science. Scientific American 283, 4 (Oct. 2000), 82--87.R. D. Thompson and A. P. Basu. 1996. Asymmetric loss functions for estimating system reliability. In Bayesian Analysis in Statistics and Econometrics. John Wiley & Sons, 471--482.L. Torgo. 2005. Regression error characteristic surfaces. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 697--702.L. Torgo. 2010. Data Mining with R. Chapman and Hall/CRC Press.L. Torgo and R. Ribeiro. 2007. Utility-based regression. Knowledge Discovery in Databases: PKDD 2007. 597--604.L. Torgo and R. Ribeiro. 2009. Precision and recall for regression. In Discovery Science. Springer, 332--346.P. Turney. 2000. Types of cost in inductive concept learning. Canada National Research Council Publications Archive.L. Wasserman. 2006. All of Nonparametric Statistics. Springer-Verlag, New York.M. P. Wellman, D. M. Reeves, K. M. Lochner, and Y. Vorobeychik. 2004. Price prediction in a trading agent competition. Journal of Artificial Intelligence Research 21 (2004), 19--36.K. Yu and M. C. Jones. 2004. Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association 99, 465 (2004), 139--144.B. Zadrozny and C. Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 694--699.A. Zellner. 1986. Bayesian estimation and prediction using asymmetric loss functions. Journal of the American Statistical Association (1986), 446--451.H. Zhao, A. P. Sinha, and G. Bansal. 2011. An extended tuning method for cost-sensitive regression and forecasting. Decision Support Systems

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

The Transporter Classification Database: recent advances

The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users

Low level of physical activity in women with rheumatoid arthritis is associated with cardiovascular risk factors but not with body fat mass - a cross sectional study

<p>Abstract</p> <p>Background</p> <p>As many patients with rheumatoid arthritis (RA) have increased fat mass (FM) and increased frequency of cardiovascular diseases we evaluated if total physical activity (MET-hours) had impact on body composition and cardiovascular risk factors in women with RA.</p> <p>Methods</p> <p>Sixty-one out-ward RA women, 60.8 (57.3-64.4) years, answered a self-administered questionnaire, to estimate total daily physical activity during the previous year. Physical activity level was given as metabolic equivalents (MET) × h/day. Diet content was assessed by a food frequency questionnaire and body composition by whole-body dual-energy X-ray absorptiometry. Blood lipids and antibodies against phosphorylcholine (anti-PC) were determined.</p> <p>Results</p> <p>Forty-one percent of the women had BMI > 25, 6% were centrally obese and 80% had FM% > 30%. The median (IQR) total physical activity was 40.0 (37.4-47.7), i.e. the same activity level as healthy Swedish women in the same age. Total physical activity did not significantly correlate with disease activity, BMI or FM%. Disease activity, BMI and FM% did not differ between those in the lowest quartile of total physical activity and those in the highest quartile. However, the women in the lowest quartile of physical activity had lower HDL (p = 0.05), Apo A1 (p = 0.005) and atheroprotective natural anti-PC (p = 0.016) and higher levels of insulin (p = 0.05) and higher frequency of insulin resistance than those in the highest quartile. Women in the lowest quartile consumed larger quantities of saturated fatty acids than those in the highest quartile (p = 0.042), which was associated with high oxidized low-density lipoprotein (oxLDL).</p> <p>Conclusion</p> <p>This cross sectional study demonstrated that RA women with fairly low disease activity, good functional capacity, high FM and high frequency of central obesity had the same total physical activity level as healthy Swedish women in the same age. The amount of total physical activity was not associated with functional capacity or body composition. However, low total physical activity was associated with dyslipidemia, insulin resistance, low levels of atheroprotective anti-PC and consumption of saturated fatty acids, which is of interest in the context of increased frequency of cardiovascular disease in RA.</p

A falls prevention programme to improve quality of life, physical function and falls efficacy in older people receiving home help services: study protocol for a randomised controlled trial

BACKGROUND: Falls and fall-related injuries in older adults are associated with great burdens, both for the individuals, the health care system and the society. Previous research has shown evidence for the efficiency of exercise as falls prevention. An understudied group are older adults receiving home help services, and the effect of a falls prevention programme on health-related quality of life is unclear. The primary aim of this randomised controlled trial is to examine the effect of a falls prevention programme on quality of life, physical function and falls efficacy in older adults receiving home help services. A secondary aim is to explore the mediating factors between falls prevention and health-related quality of life. METHODS: The study is a single-blinded randomised controlled trial. Participants are older adults, aged 67 or older, receiving home help services, who are able to walk with or without walking aids, who have experienced at least one fall during the last 12 months and who have a Mini Mental State Examination of 23 or above. The intervention group receives a programme, based on the Otago Exercise Programme, lasting 12 weeks including home visits and motivational telephone calls. The control group receives usual care. The primary outcome is health-related quality of life (SF-36). Secondary outcomes are leg strength, balance, walking speed, walking habits, activities of daily living, nutritional status and falls efficacy. All measurements are performed at baseline, following intervention at 3 months and at 6 months' follow-up. Sample size, based on the primary outcome, is set to 150 participants randomised into the two arms, including an estimated 15-20% drop out. Participants are recruited from six municipalities in Norway. DISCUSSION: This trial will generate new knowledge on the effects of an exercise falls prevention programme among older fallers receiving home help services. This knowledge will be useful for clinicians, for health managers in the primary health care service and for policy makers

Public health and wellbeing: A matter for the midwife?

This paper will provide a critical narrative review of public health interventions in pregnancy and the role of the midwife in public health. The historical and political context of public health and midwifery will be examined to give a background to the current midwifery public health agenda. The article will identify specific public health interventions used in pregnancy by midwives and assess how midwives perceive their role in implementing them. Midwives are important public health practitioners, who alongside other agencies can make a long-term, positive contribution to the life course of women and their families