Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Localising and quantifying damage by means of a multi-chromosome genetic algorithm

This paper presents a structural damage detection methodology based on genetic algorithms and dynamic parameters. Three chromosomes are used to codify an individual in the population. The first and second chromosomes locate and quantify damage, respectively. The third permits the self-adaptation of the genetic parameters. The natural frequencies and mode shapes are used to formulate the objective function. A numerical analysis was performed for several truss structures under different damage scenarios. The results have shown that the methodology can reliably identify damage scenarios using noisy measurements and that it results in only a few misidentified elements. (C) 2012 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

The double task of preventing malnutrition and overweight: a quasi-experimental community-based trial

Background: The Maternal-Child Pastoral is a volunteer-based community organization of the Dominican Republic that works with families to improve child survival and development. A program that promotes key practices of maternal and child care through meetings with pregnant women and home visits to promote child growth and development was designed and implemented. This study aims to evaluate the impact of the program on nutritional status indicators of children in the first two years of age. Methods: A quasi-experimental design was used, with groups paired according to a socioeconomic index, comparing eight geographical areas of intervention with eight control areas. The intervention was carried out by lay health volunteers. Mothers in the intervention areas received home visits each month and participated in a group activity held biweekly during pregnancy and monthly after birth. The primary outcomes were length and body mass index for age. Statistical analyses were based on linear and logistic regression models. Results: 196 children in the intervention group and 263 in the control group were evaluated. The intervention did not show statistically significant effects on length, but point estimates found were in the desired direction: mean difference 0.21 (95%CI −0.02; 0.44) for length-for-age Z-score and OR 0.50 (95%CI 0.22; 1.10) for stunting. Significant reductions of BMI-for-age Z-score (−0.31, 95%CI −0.49; -0.12) and of BMI-for-age > 85th percentile (0.43, 95%CI 0.23; 0.77) were observed. The intervention showed positive effects in some indicators of intermediary factors such as growth monitoring, health promotion activities, micronutrient supplementation, exclusive breastfeeding and complementary feeding. Conclusions: Despite finding effect measures pointing to effects in the desired direction related to malnutrition, we could only detect a reduction in the risk of overweight attributable to the intervention. The findings related to obesity prevention may be of interest in the context of the nutritional transition. Given the size of this study, the results are encouraging and we believe a larger study is warranted.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.