Abstract
In this paper, a meta-learning approach is proposed to suggest the best optimization technique(s) for instances of the Traveling Salesman Problem. The problem is represented by a dataset where each example is associated with one of the instances. Thus, each example contains characteristics of an instance and is labeled with the name of the technique(s) that obtained the best solution for this instance. Since the best solution can be obtained by more than one technique, an example may have more than one label. Therefore, the meta-learning problem is addressed as a multi-label classification problem. Experiments with 535 instances of the problem were performed to evaluate the proposed approach, which produced promising results. © 2010 IEEE.

Abstract
Nowadays, microarray has become a fairly common tool for simultaneously inspecting the behavior of thousands of genes. Researchers have employed this technique to understand various biological phenomena. One straightforward use of such technology is identifying the class membership of the tissue samples based on their gene expression profiles. This task has been handled by a number of computational methods. In this paper, we provide a comprehensive evaluation of 7 commonly used algorithms over 6S publicly available gene expression datasets. The focus of the study was on comparing the performance of the algorithms in an efficient and sound manner, supporting the prospective users on how to proceed to choose the most adequate classification approach according to their investigation goals.

Abstract
Recently, meta-learning techniques have been employed to the problem of algorithm recommendation for gene expression data classification. Due to their flexibility, the advice provided to the user was in the form of rankings, which are able to express a preference order of Machine Learning algorithms accordingly to their expected relative performance. Thus, choosing how to learn accurate rankings arises as a key research issue. In this work, the authors empirically evaluated 2 general approaches for ranking prediction and extended them. The results obtained for 49 publicly available microarray datasets indicate that the extensions introduced were very beneficial to the quality of the predicted rankings. © 2010 Springer-Verlag.

Abstract
In large and complex organizations, the development of workflow automation projects is hard. In some cases, a first important step in that direction is the automation of the routing of incoming documents. In this paper, we describe a project to develop a system for the first routing of incoming letters to the right department within a large, public portuguese institution. We followed a data mining approach, where data representing previous routings were analyzed to obtain a model that can be used to route future documents. The approach followed was strongly influenced by some of the limitations imposed by the customer: the budget available was small and the solution should be developed in SQL to facilitate integration with the existing system. The system developed was able to obtain satisfactory results. However, as in any Data Mining project, most of the effort was dedicated to activities other than modelling (e.g., data preparation), which means that there is still plenty of room for improvement.

Abstract
The problem of learning label rankings is receiving increasing attention from several research communities. A number of common learning algorithms have been adapted for this task, including k-Nearest Neighbours (k-NN) and decision trees. Following this line, we propose an adaptation of the naive Bayes classification algorithm for the label ranking problem. Our main idea lies in the use of similarity between the rankings to replace the concept of probability. We empirically test the proposed method on some metalearning problems that consist of relating characteristics of learning problems to the relative performance of learning algorithms. Our method generally performs better than the baseline indicating that it is able to identify some of the underlying patterns in the data.

Abstract