A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

A genetic algorithm to ensemble feature selection

We are going to implement the "GA-SEFS" by Tsymbal and analyse experimentally its performance depending on the classifier algorithms used in the fitness function (NB, MNge, SMO). We are also going to study the effect of adding to the fitness function a measure to control complexity of the base classifiers.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Single period Markowitz portfolio selection, performance gauging and duality: a variation on Luenberger's shortage function

Markowitz portfolio theory (1952) has induced research into the efficiency of portfolio management. This paper studies existing nonparametric efficiency measurement approaches for single period portfolio selection from a theoretical perspective and generalises currently used efficiency measures into the full mean-variance space. Therefore, we introduce the efficiency improvement possibility function (a variation on the shortage function), study its axiomatic properties in the context of Markowitz efficient frontier, and establish a link to the indirect mean-variance utility function. This framework allows distinguishing between portfolio efficiency and allocative efficiency. Furthermore, it permits retrieving information about the revealed risk aversion of investors. The efficiency improvement possibility function thus provides a more general framework for gauging the efficiency of portfolio management using nonparametric frontier envelopment methods based on quadratic optimisation.

Parametric and semiparametric estimation of sample selection models: an empirical application to the female labour force in Portugal

This comment corrects the errors in the estimation process that appear in Martins (2001). The first error is in the parametric probit estimation, as the previously presented results do not maximize the log-likelihood function. In the global maximum more variables become significant. As for the semiparametric estimation method, the kernel function used in Martins (2001) can take on both positive and negative values, which implies that the participation probability estimates may be outside the interval [0,1]. We have solved the problem by applying local smoothing in the kernel estimation, as suggested by Klein and Spady (1993).

I want you! An experiment studying the selection effect when assigning distributive power.

We study whether selection affects motivation. In our experiment subjects first answer a personality questionnaire. They then play a 3-person game. One of the three players decides between an outside option assigning him a positive amount, but leaving the two others empty-handed and allowing one of the other two players to distribute a pie. Treatments differ in the procedure by which distributive power is assigned: to a randomly determined or to a knowingly selected partner. Before making her decision the selecting player could consult the personality questionnaire of the other two players. Results show that knowingly selected players keep less for themselves than randomly selected ones and reward the selecting player more generously.

Self-selection consistent functions

This paper studies collective choice rules whose outcomes consist of a collection of simultaneous decisions, each one of which is the only concern of some group of individuals in society. The need for such rules arises in different contexts, including the establishment of jurisdictions, the location of multiple public facilities, or the election of representative committees. We define a notion of allocation consistency requiring that each partial aspect of the global decision taken by society as a whole should be ratified by the group of agents who are directly concerned with this particular aspect. We investigate the possibility of designing envy-free allocation consistent rules, we also explore whether such rules may also respect the Condorcet criterion.

Entry and market selection of firms: a laboratory study

We study competition in experimental markets in which two incumbents face entry by three other firms. Our treatments vary with respect to three factors: sequential vs. block or simultaneous entry, the cost functions of entrants and the amount of time during which incumbents are protected from entry. Before entry incumbents are able to collude in all cases. When all firms' costs are the same entry always leads consumer surplus and profits to their equilibrium levels. When entrants are more efficient than incumbents, entry leads consumer surplus to equilibrium. However, total profits remain below equilibrium, due to the fact that the inefficient incumbents produce too much and efficient entrants produce too little. Market behavior is satisfactory from the consumers' standpoint, but does not yield adequate signals to other potential entrants. These results are not affected by whether entry is simultaneous or sequential. The length of the incumbency phase does have some subtle effects.

Wealth constraints, skill prices or networks: what determines emigrant selection?

The productive characteristics of migrating individuals, emigrant selection, affect welfare. The empirical estimation of the degree of selection suffers from a lack of complete and nationally representative data. This paper uses a new and better dataset to address both issues: the ENET (Mexican Labor Survey), which identifies emigrants right before they leave and allows a direct comparison to non-migrants. This dataset presents a relevant dichotomy: it shows on average negative selection for Mexican emigrants to the United States for the period 2000-2004 together with positive selection in Mexican emigration out of rural Mexico to the United States in the same period. Three theories that could explain this dichotomy are tested. Whereas higher skill prices in Mexico than in the US are enough to explain negative selection in urban Mexico, its combination with network effects and wealth constraints is required to account for positive selection in rural Mexico.

New evidence on emigrant selection

This paper examines the extent to which Mexican emigrants to the United States are negatively selected, that is, have lower skills than individuals who remain in Mexico. Previous studies have been limited by the lack of nationally representative longitudinal data. This one uses a newly available household survey, which identifies emigrants before they leave and allows a direct comparison to non-migrants. I find that, on average, US bound Mexican emigrants from 2000 to 2004 earn a lower wage and have less schooling years than individuals who remain in Mexico, evidence of negative selection. This supports the original hypothesis of Borjas (AER, 1987) and argues against recent findings, notably those of Chiquiar and Hanson (JPE, 2005). The discrepancy with the latter is primarily due to an under-count of unskilled migrants in US sources and secondarily to the omission of unobservables in their methodology.

Measuring intergenerational earnings mobility in Spain: a selection-bias-free approach

This paper analyses intergenerational earnings mobility in Spain correcting for different selection biases. We address the co-residence selection problem by combining information from two samples and using the two-sample two-stage least square estimator. We find a small decrease in elasticity when we move to younger cohorts. Furthermore, we find a higher correlation in the case of daughters than in the case of sons; however, when we consider the employment selection in the case of daughters, by adopting a Heckman-type correction method, the diference between sons and daughters disappears. By decomposing the sources of earnings elasticity across generations, we find that the correlation between child's and father's occupation is the most important component. Finally, quantile regressions estimates show that the influence of the father's earnings is greater when we move to the lower tail of the offspring's earnings distribution, especially in the case of daughters' earnings.

Selection bias and unobservable heterogeneity applied at the wage equation of European married women

This paper utilizes a panel data sample selection model to correct the selection in the analysis of longitudinal labor market data for married women in European countries. We estimate the female wage equation in a framework of unbalanced panel data models with sample selection. The wage equations of females have several potential sources of.

Scene classifier for real time road detection

L’objectiu principal del projecte és el de classificar escenes de carretera en funció del contingut de les imatges per així poder fer un desglossament sobre quin tipus de situació tenim en el moment. És important que fixem els paràmetres necessaris en funció de l’escenari en què ens trobem per tal de treure el màxim rendiment possible a cada un dels algoritmes. La seva funcionalitat doncs, ha de ser la d’avís i suport davant els diferents escenaris de conducció. És a dir, el resultat final ha de contenir un algoritme o aplicació capaç de classificar les imatges d’entrada en diferents tipus amb la màxima eficiència espacial i temporal possible. L’algoritme haurà de classificar les imatges en diferents escenaris. Els algoritmes hauran de ser parametritzables i fàcilment manejables per l’usuari. L’eina utilitzada per aconseguir aquests objectius serà el MATLAB amb les toolboxs de visió i xarxes neuronals instal·lades.