Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Heuristic methods for the single machine scheduling problem with different ready times and a common due date

The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

Are mental health problems and depression associated with bruxism in children?

Renner AC, da Silva AAM, Rodriguez JDM, Simoes VMF, Barbieri MA, Bettiol H, Thomaz EBAF, Saraiva MC. Are mental health problems and depression associated with bruxism in children? Community Dent Oral Epidemiol 2011. (C) 2011 John Wiley & Sons A/S Abstract Objectives: Previous studies have found an association between bruxism and emotional and behavioral problems in children, but reported data are inconsistent. The objective of this study was to estimate the prevalence of bruxism, and of its components clenching and grinding, and its associations with mental problems and depression. Methods: Data from two Brazilian birth cohorts were analyzed: one from 869 children in Ribeirao Preto RP (Sao Paulo), a more developed city, and the other from 805 children in Sao Luis SL (Maranhao). Current bruxism evaluated by means of a questionnaire applied to the parents/persons responsible for the children was defined when the habit of tooth clenching during daytime and/or tooth grinding at night still persisted until the time of the assessment. Additionally, the lifetime prevalence of clenching during daytime only and grinding at night only was also evaluated. Mental health problems were investigated using the Strength and Difficulties Questionnaire (SDQ) and depression using the Childrens Depression Inventory (CDI). Analyses were carried out for each city: with the SDQ subscales (emotional symptoms, conduct problems, peer problems, attention/hyperactivity disorder), with the total score (sum of the subscales), and with the CDI. These analyses were performed considering different response variables: bruxism, clenching only, and grinding only. The risks were estimated using a Poisson regression model. Statistical inferences were based on 95% confidence intervals (95% CI). Results: There was a high prevalence of current bruxism: 28.7% in RP and 30.0% in SL. The prevalence of clenching was 20.3% in RP and 18.8% in SL, and grinding was found in 35.7% of the children in RP and 39.1% in SL. Multivariable analysis showed a significant association of bruxism with emotional symptoms and total SDQ score in both cities. When analyzed separately, teeth clenching was associated with emotional symptoms, peer problems, and total SDQ score; grinding was significantly associated with emotional symptoms and total SDQ score in RP and SL. Female sex appeared as a protective factor for bruxism, and for clenching and grinding in RP. Furthermore, maternal employment outside the home and white skin color of children were associated with increased prevalence of teeth clenching in SL. Conclusions: Mental health problems were associated with bruxism, with teeth clenching only and grinding at night only. No association was detected between depression and bruxism, neither clenching nor grinding. But it is necessary to be cautious regarding the inferences from some of our results.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.