Diana Martin

Bio: Diana Martin is an academic researcher from Polytechnic José Antonio Echeverría. The author has contributed to research in topics: Evolutionary algorithm & Association rule learning. The author has an hindex of 4, co-authored 5 publications receiving 228 citations.

A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules

Abstract: Most of the algorithms for mining quantitative association rules focus on positive dependencies without paying particular attention to negative dependencies. The latter may be worth taking into account, however, as they relate the presence of certain items to the absence of others. The algorithms used to extract such rules usually consider only one evaluation criterion in measuring the quality of generated rules. Recently, some researchers have framed the process of extracting association rules as a multiobjective problem, allowing us to jointly optimize several measures that can present different degrees of trade-off depending on the dataset used. In this paper, we propose MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost. To accomplish this, our proposal extends a recent multiobjective evolutionary algorithm based on decomposition to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule, while introducing an external population and a restarting process to store all the nondominated rules found and to improve the diversity of the rule set obtained. Moreover, this proposal maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset. The effectiveness of the proposed approach is validated over several real-world datasets.

A multi-objective evolutionary algorithm for mining quantitative association rules

Abstract: Data mining is most commonly used in attempts to induce association rules from database. Recently, some researchers have suggested the extraction of association rules as a multi-objective problem, removing some of the limitations of current approaches. In this way, we can jointly optimize quality measures which can present different degrees of tradeoff depending on the database used and the type of information can be extracted from it. In this work, we extend the well-known multi-objective evolutionary algorithms NSGA-II to perform an evolutionary learning of the intervals of attributes and a condition selection in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy. To do that, this method considers three objectives, maximize the interestingness, comprehensibility and performance. Moreover, this method follows a database-independent approach which does not rely upon minimum support and minimum confidence thresholds. The results obtained over two real-world databases demonstrate the effectiveness of the proposed approach.

A Novel Initial Population Construction Heuristic for the DINOS Subgroup Discovery Algorithm

Abstract: Evolutionary algorithms for subgroup discovery usually randomly initialize the population, which often causes them to spend part of their time evaluating unpromising solutions. This situation causes the algorithm to take more time to converge to good solutions. In this paper, we present a new initial population construction heuristic for DINOS, a genetic subgroup discovery algorithm that mines non-redundant subgroups with high quality in a short time. The proposed heuristic is based on the generation of a collection of decision trees, allowing to obtain an initial population in which all the rules are valid and with a large coverage of the database. The quality of these rules is also high and they contain a large diversity in the attributes used, allowing to deal with problems having a large number of dimensions. The experiments carried out show that the new method allows mining more high-quality and diverse subgroups in a slightly higher computational time.

Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design

Abstract: In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to group the population into multiple clusters, and the divergent operation uses this cluster information to generate new individuals. However, this mechanism is inefficient to regulate the exploration and exploitation search. This article first analyzes the main factors that influence the performance of BSO and then proposes an orthogonal learning framework to improve its learning mechanism. In this framework, two orthogonal design (OD) engines (i.e., exploration OD engine and exploitation OD engine) are introduced to discover and utilize useful search experiences for performance improvements. In addition, a pool of auxiliary transmission vectors with different features is maintained and their biases are also balanced by the OD decision mechanism. Finally, the proposed algorithm is verified on a set of benchmarks and is adopted to resolve the quantitative association rule mining problem considering the support, confidence, comprehensibility, and netconf. The experimental results show that the proposed approach is very powerful in optimizing complex functions. It not only outperforms previous versions of the BSO algorithm but also outperforms several famous OD-based algorithms.

Erratum: A species conserving genetic algorithm for multimodal function optimization (Evolutionary Computation)

Abstract: This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach

Abstract: In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.