Alexandre Plastino

A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains

First proposed in 2009, the classifier chains model (CC) has become one of the most influential algorithms for multi-label classification. It is distinguished by its simple and effective approach to exploit label dependencies. The CC method involves the training of q single-label binary classifiers, where each one is solely responsible for classifying a specific label in ll, ..., lq. These q classifiers are linked in a chain, such that each binary classifier is able to consider the labels predicted by the previous ones as additional information at classification time. The label ordering has a strong effect on predictive accuracy, however it is decided at random and/or combining random orders via an ensemble. A disadvantage of the ensemble approach consists of the fact that it is not suitable when the goal is to generate interpretable classifiers. To tackle this problem, in this work we propose a genetic algorithm for optimizing the label ordering in classifier chains. Experiments on diverse benchmark datasets, followed by the Wilcoxon test for assessing statistical significance, indicate that the proposed strategy produces more accurate classifiers.

Hybridization of GRASP Metaheuristic with Data Mining Techniques

In this work, we propose a hybridization of GRASP metaheuristic that incorporates a data mining process. We believe that patterns obtained from a set of sub-optimal solutions, by using data mining techniques, can be used to guide the search for better solutions in metaheuristics procedures. In this hybrid GRASP proposal, after executing a significant number of GRASP iterations, the data mining process extracts patterns from an elite set of solutions which will guide the following iterations. To validate this proposal we have worked on the Set Packing Problem as a case study. Computational experiments, comparing traditional GRASP and different hybrid approaches, show that employing frequent patterns mined from an elite set of solutions conducted to better results. Besides, additional performed experiments evidence that data mining strategies accelerate the process of finding good solutions.

Applications of the DM‐GRASP heuristic: a survey

Recent research has shown that the hybridization of metaheuristics is a powerful mechanism to develop more robust and efficient methods to solve hard optimization problems. The combination of different techniques and concepts behind metaheuristics, if well designed, has the potential to exploit their advantages while diminishing their drawbacks, which results in methods suited to a more diverse set of real problems. The DM-GRASP heuristic is one such hybrid method that has achieved promising results. It is a hybrid version of the GRASP metaheuristic that incorporates a data-mining process. In this work, we review how this hybridization was designed and survey the results of its practical applications evaluated until now.

New heuristics for the maximum diversity problem

Abstract The maximum diversity problem (MDP) consists of identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics among the elements of the subset. The identification of such solution is an NP-hard problem. Some heuristics are available to obtain approximate solutions for this problem. In this paper, we propose different GRASP heuristics for the MDP, using distinct construction procedures and including a path-relinking technique. Performance comparison among related work and the proposed heuristics is provided. Experimental results show that the new GRASP heuristics are quite robust and are able to find high-quality solutions in reasonable computational times.

A hybrid GRASP with data mining for the maximum diversity problem

The maximum diversity problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics among themselves. The identification of such solution is an NP-hard problem. In this work, we propose a hybrid GRASP metaheuristic for the MDP that incorporates a data mining process. Data mining refers to the extraction of new and potentially useful knowledge from datasets in terms of patterns and rules. We believe that data mining techniques can be used to extract patterns that represent characteristics of sub-optimal solutions of a combinatorial optimization problem. Therefore these patterns can be used to guide the search for better solutions in metaheuristics procedures. Performance comparison between related work and the proposed hybrid heuristics is provided. Experimental results show that the new hybrid GRASP is quite robust and, mainly, this strategy is able to find high-quality solutions in less computational time.

GRASP with path-relinking for the maximum diversity problem

The Maximum Diversity Problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics between themselves. The identification of such solution is an NP-hard problem. This paper presents a GRASP heuristic associated with the path-relinking technique developed to obtain high-quality solutions for this problem in a competitive computational time. Experimental results illustrate the effectiveness of using the path-relinking method to improve results generated by pure GRASP.

A hybrid data mining metaheuristic for the p -median problem

Metaheuristics represent an important class of techniques to solve, approximately, hard combinatorial optimization problems for which the use of exact methods is impractical. In this work, we propose a hybrid version of the Greedy Randomized Adaptive Search Procedures (GRASP) metaheuristic, which incorporates a data mining process, to solve the p-median problem. We believe that patterns obtained by a data mining technique, from a set of suboptimal solutions of a combinatorial optimization problem, can be used to guide metaheuristic procedures in the search for better solutions. Traditional GRASP is an iterative metaheuristic which returns the best solution reached over all iterations. In the hybrid GRASP proposal, after executing a significant number of iterations, the data mining process extracts patterns from an elite set of suboptimal solutions for the p-median problem. These patterns present characteristics of near optimal solutions and can be used to guide the following GRASP iterations in the search through the combinatorial solution space. Computational experiments, comparing traditional GRASP and different data mining hybrid proposals for the p-median problem, showed that employing patterns mined from an elite set of suboptimal solutions made the hybrid GRASP find better results. Besides, the conducted experiments also evidenced that incorporating a data mining technique into a metaheuristic accelerated the process of finding near optimal and optimal solutions.

A hybrid data mining GRASP with path-relinking

The exploration of hybrid metaheuristics-combination of metaheuristics with concepts and processes from other research areas-has been an important trend in combinatorial optimization research. An instance of this study is the hybrid version of the GRASP metaheuristic that incorporates a data mining process. Traditional GRASP is an iterative metaheuristic which returns the best solution reached over all iterations. In the hybrid GRASP proposal, after executing a significant number of iterations, the data mining process extracts patterns from an elite set of sub-optimal solutions for the optimization problem. These patterns present characteristics of near optimal solutions and can be used to guide the following GRASP iterations in the search through the combinatorial solution space. The hybrid data mining GRASP has been successfully applied for different combinatorial problems: the set packing problem, the maximum diversity problem, the server replication for reliable multicast problem and the p-median problem. In this work, we show that, not only the traditional GRASP, but also GRASP improved with the path-relinking heuristic-a memory-based intensification strategy-could benefit from exploring a data mining procedure. Computational experiments, comparing traditional GRASP with path-relinking and different path-relinking hybrid proposals, showed that employing the combination of path-relinking and data mining made the GRASP find better results in less computational time. Another contribution of this work is the application of the path-relinking hybrid proposal for the 2-path network design problem, which improved the state-of-the-art solutions for this problem.

Developing SPMD applications with load balancing

The central contribution of this work is SAMBA (Single Application, Multiple Load Balancing), a framework for the development of parallel SPMD (single program, multiple data) applications with load balancing. This framework models the structure and the characteristics common to different SPMD applications and supports their development. SAMBA also contains a library of load balancing algorithms. This environment allows the developer to focus on the specific problem at hand. Special emphasis is given to the identification of appropriate load balancing strategies for each application. Three different case studies were used to validate the functionality of the framework: matrix multiplication, numerical integration, and a genetic algorithm. These applications illustrate its ease of use and the relevance of load balancing. Their choice was oriented by the different load imbalance factors they present and by their different task creation mechanisms. The computational experiments reported for these case studies made possible the validation of SAMBA and the comparison, without additional reprogramming costs, of different load balancing strategies for each of them. The numerical results and the elapsed times measurements show the importance of using an appropriate load balancing algorithm and the associated reductions that can be achieved in the elapsed times. They also illustrate that the most suitable load balancing strategy may vary with the type of application and with the number of available processors. Besides the support to the development of SPMD applications, the facilities offered by SAMBA in terms of load balancing play also an important role in terms of the development of efficient parallel implementations.

Categorizing feature selection methods for multi-label classification

In many important application domains such as text categorization, biomolecular analysis, scene classification and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research on feature selection methods that allow the identification of relevant and informative features for multi-label classification. However, the methods proposed for this task are scattered in the literature, with no common framework to describe them and to allow an objective comparison. Here, we revisit a categorization of existing multi-label classification methods and, as our main contribution, we provide a comprehensive survey and novel categorization of the feature selection techniques that have been created for the multi-label classification setting. We conclude this work with concrete suggestions for future research in multi-label feature selection which have been derived from our categorization and analysis.