Abstract
Several meta-learning approaches have been developed for the problem of algorithm selection. In this context, it is of central importance to collect a sufficient number of datasets to be used as meta-examples in order to provide reliable results. Recently, some proposals to generate datasets have addressed this issue with successful results. These proposals include datasetoids, which is a simple manipulation method to obtain new datasets from existing ones. However, the increase in the number of datasets raises another issue: in order to generate meta-examples for training, it is necessary to estimate the performance of the algorithms on the datasets. This typically requires running all candidate algorithms on all datasets, which is computationally very expensive. One approach to address this problem is the use of an active learning approach to meta-learning, termed active meta-learning. In this paper we investigate the combined use of an active meta-learning approach based on an uncertainty score and datasetoids. Based on our results, we conclude that the accuracy of our method is very good results with as little as 10% to 20% of the meta-examples labeled.

Abstract
Several meta-learning approaches have been developed for the problem of algorithm selection. In this context, it is of central importance to collect a sufficient number of datasets to be used as meta-examples in order to provide reliable results. Recently, some proposals to generate datasets have addressed this issue with successful results. These proposals include datasetoids, which is a simple manipulation method to obtain new datasets from existing ones. However, the increase in the number of datasets raises another issue: in order to generate meta-examples for training, it is necessary to estimate the performance of the algorithms on the datasets. This typically requires running all candidate algorithms on all datasets, which is computationally very expensive. In a recent paper, active meta-learning has been used to address this problem. An uncertainty sampling method for the k-NN algorithm using a least confidence score based on a distance measure was employed. Here we extend that work, namely by investigating three hypotheses: 1) is there advantage in using a frequency-based least confidence score over the distance-based score? 2) given that the meta-learning problem used has three classes, is it better to use a margin-based score? and 3) given that datasetoids are expected to contain some noise, are better results achieved by starting the search with all datasets already labeled? Some of the results obtained are unexpected and should be further analyzed. However, they confirm that active meta-learning can significantly reduce the computational cost of meta-learning with potential gains in accuracy.

Abstract
Several optimization methods can find good solutions for different instances of the Traveling Salesman Problem (TSP). Since there is no method that generates the best solution for all instances, the selection of the most promising method for a given TSP instance is a difficult task. This paper describes a meta-learning-based approach to select optimization methods for the TSP. Multilayer perceptron (MLP) networks are trained with TSP examples. These examples are described by a set of TSP characteristics and the cost of solutions obtained by a set of optimization methods. The trained MLP network model is then used to predict a ranking of these methods for a new TSP instance. Correlation measures are used to compare the predicted ranking with the ranking previously known. The obtained results suggest that the proposed approach is promising. © 2011 IEEE.

Abstract
The design, production and distribution of small series of health fashionable goods for specific target groups of wide impact in terms of market for the European industry as elderly, disables, diabetics and obese people represents a challenging opportunity for European companies which are asked to supply the demand with affordable price and eco-compatible products. Added to this challenge, textile, clothing and footwear manufactures seek for innovative collaborative networking solutions that could provide an entire digital life-cycle for the products and services required by the market. Aligned with this need, the EU CoReNet project aims to design and develop a new smart collaborative consumer-driven framework with the related services and components. This paper addresses the multidisciplinary complexity of customer-oriented and eco-friendly networks for health fashionable goods in particular addressing business requirements analysis, value chain issues, co-planning production and co-design topics in collaborative business processes tailored for high variability of the consumers demand and expectations.

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