Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel Local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the Local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a Local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k-1 records, such that the sum of the within-group squared error (SSE) is minimized. We propose a Local search algorithm which iteratively satisfies the constraints of the optimal solution of the problem. The algorithm solves the problem in O (n2) time. Experimental results on real and synthetic data sets with different distributions demonstrate the effectiveness of the method in producing useful protected data sets.

One of the problems with traditional genetic algorithms is its premature convergence that makes them incapable of searching good solutions of the problem. A memetic algorithm (MA) which is an extension of the traditional genetic algorithm uses a Local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or to reach solutions that would otherwise be unreachable by evolution or a Local search method alone. In this paper, a memetic algorithm based on learning automata (LA) and memetic algorithm, called LA-MA, is introduced. This algorithm is composed of two parts, genetic section and memetic section. Evolution is performed in genetic section and Local search is performed in memetic section. The basic idea of LA-MA is to use learning automata during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by Local search. To evaluate the efficiency of LA-MA, it has been used to solve two optimization problems: OneMax and graph isomorphism problems. The results of computer experimentations have shown that different versions of LA-MA outperform the others in terms of quality of solution and rate of convergence.

This paper considers innovation search strategy as problem-solving activities by firms that involve the screening, creation and re-use of ideas, modification, and intelligent rejection of new and old know-how. Its aim is to investigate existing innovation search strategies of Iranian industrial firms and analyze the affecting factors on decision of firms to select scientific institutions in particular universities to acquisition of new economic knowledge on a theoretical framework based. In our empirical study of 39 Iranian industrial firms based on a survey performed by IROST, found that there are weak linkage between firms and scientific institutions. The results suggest that firms with higher basic research capabilities have likely better linkage with the scientific institutions.

Dear Editor, We appreciate the interest of the authors in our article entitled “ A productive proposed search syntax for health disaster preparedness research” . They have rightly emphasized on the standard reporting of systematic reviews. However, as it is clear from the title and objective of the published article, we did not report results of a systematic review, our article instead aimed to present a syntax validation process which guide with creating a proper search strategy for systematic reviews on disaster preparedness [1-4]....