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Abstract: Ant Colony Optimization (ACO) is a bioinspired metaheuristic based on ants foraging used to solve different classes of problems. In this paper, we show how, using a Two-Stage approach the quality of the solutions of ACO is improved. The Two-Stage approach can be applied to different ACO. The performance of this new approach is studied in the Traveling Salesman Problem and Quadratic Assignment Problem. The experimental results show that the obtained solutions are improved both problems using the Two-Stage approach. Several statistical procedures are applied to show the effect of this new approach. [Copyright &y& Elsevier]

Abstract: Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics. The approaches to deal with the problem of imbalanced datasets fall into two major categories: data sampling and algorithmic modification. Cost-sensitive learning solutions incorporating both the data and algorithm level approaches assume higher misclassification costs with samples in the minority class and seek to minimize high cost errors. Nevertheless, there is not a full exhaustive comparison between those models which can help us to determine the most appropriate one under different scenarios. The main objective of this work is to analyze the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches. We will show, by means of a statistical comparative analysis, that we cannot highlight an unique approach among the rest. This will lead to a discussion about the data intrinsic characteristics of the imbalanced classification problem which will help to follow new paths that can lead to the improvement of current models mainly focusing on class overlap and dataset shift in imbalanced classification. [Copyright &y& Elsevier]