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Clustering is an important unsupervised data analysis technique, which divides data objects into clusters based on similarity. Clustering has been studied and applied in many different fields, including pattern recognition, data mining, decision science and statistics. Clustering algorithms can be mainly classified as hierarchical and partitional clustering approaches. Partitioning around medoids...
Previous research on dimensionality reduction has shown that global and local information is both important for capturing the crucial features of data sets. In this paper, we develop a new approach that can effectively retain both global and local features of a dataset for supervised dimensionality reduction. A new quadratic measure is developed to accurately describe the local features of a dataset...
Feature selection is an important tool used in data reduction; it aims at improving efficiency in many machine-learning algorithms by choosing a small set of informative features among the whole dataset. Feature selection algorithms can be classified in three major categories: Filter, Wrapper and Embedded. In this paper, we proposed a new hybrid filter-wrapper algorithm of feature selection based...
Since their introduction, random forests (RFs) have successfully been employed in a vast array of application areas. Fairly recently, a number of algorithms that are related to Breiman's original Forest-RI algorithm have been proposed in the literature. In this paper we conduct a meta-analysis of all (34) 2001–2015 papers that could be found in which a novel RF algorithm was proposed and compared...
One of the important problems in Decision Making Support Systems is recognition (classification) of a new sample according to the previous knowledge. There are a lot of methods and approaches for solving this problem. Classification Rules that can be constructed based on Decision Trees or Fuzzy Decision Trees (FDT) are one of them. In this paper, an algorithm for FDT induction based on Cumulative...
Many real-world datasets suffer from the problem of class imbalance, i.e., they have a minority class being only a small portion of the whole dataset. Under-sampling techniques, e.g., EasyEnsemble (EE), present an efficient approach to imbalanced classification problems. However, imbalance is not the only factor that harms the performance of conventional classifiers. The presence of noises is another...
Machine learning with concept drifting attracts a lot of attention in recent years. However, there are only a few works on concept drift learning with imbalanced data. The Learn++.NSE, the Learn++.NIE, and the Learn++.CDS from the Learn++ family are three state-of-the-art learning algorithms designed to deal with machine learning with concept drifting. In this work, we firstly give a brief introduction...
The use of meta-heuristic algorithms for solving real world problems increases day by day. Bat Algorithm is a meta-heuristic optimization algorithm based on the echolocation behavior of microbats. Bat Algorithm has advantage which claimed to provide very quick convergence at a very initial stage by automatic switching from exploration to exploitation. Hereby, algorithm loses exploration capability...
Network packet classification is the central building block for important services such as QoS routing and firewalling. Accordingly, a wide range of classification schemes has been proposed, each with its own specific set of characteristics. But while novel algorithms keep being developed at a high pace, there barely exists tool support for proper benchmarking, which makes it hard for researchers...
In this paper, we present an efficient algorithm for solving optimization problems, which is based on gravitational search algorithm (GSA). In the proposed technique, called Two-Step method, the best solution of position will be considered and calculated with another agents, and the fitness of extended agents are compared with agents in original gravitation field, which can reinforce the exploration...
This paper develops a new fuzzy harmony search algorithm (FHS) for solving optimization problems. FHS employs a novel method using fuzzy logic for adaptation of the harmony memory accepting parameter that enhances the accuracy and convergence rate of the harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning...
These days, many traditional end-user applications are said to “run fast enough” on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains...
In this paper, in order to prove the effectiveness of the Imperialist Competitive Algorithm — a socio-political inspired algorithm-on finding the optimal solution for different kinds of minimization functions as well as different kinds of landscapes. The reliability and quality of solutions for mathematical minimization functions of the ICA is evaluated by seven distinct benchmark functions where...
The maximum clique question is a paradigmatic combinatorial optimization. In order to get better solution, cuckoo search, a new meta-heuristic method was introduced. The cuckoo search was used to get better candidate solutions in this hybrid algorithm. Then, hybrid algorithm is compared with VNS and SA by standard DIMACS data sets, result shows that hybrid algorithm can get better solution in most...
In the paper a novel cuckoo search (CS) algorithm based on the idea of opposition (OCS) algorithm is proposed and attempts to increase the exploration efficiency of the solution space in order to solve optimization problems. the modifications focus on the solution construction phase of the CS algorithm, which organically merges the opposition-based learning into CS algorithm and the OCS algorithm...
Bayesian network (BN) structure learning is an NP hard problem. Search and score algorithms are one of the main approaches proposed for learning BN structure from data. Previous research has shown that the relative performances of such algorithms are problem dependent and that fitness landscape analysis can be used to characterize the difficulty of the search for different scoring functions. In this...
Most of constraint handling papers have focused on the selection of individuals by trade-off the feasible and infeasible regions. This paper studies the effect of two kinds of reproduction in constraint multiobjective optimization. It compares a probabilistic model-based multiobjective evolutionary algorithm to a genetic algorithm. They all use a min-max selection strategy as the main frame structure...
Due to the ever increasing performance gap between the processor and the main memory, it becomes crucial to bridge that gap by designing an efficient memory hierarchy capable of reducing the average memory access time. The cache replacement algorithm plays a central role in designing an efficient memory hierarchy. Many of the recent studies in cache replacement algorithms have focused on improving...
Community detection is now playing a significant role in the discovery of underlying structures of social networks. This problem has been proved to be very hard and not been satisfactorily solved yet. Most of the algorithms proposed so far tend to maximize the number of intra-cluster edges, but ignore the importance of the core nodes within clusters. In contrast, this paper proposes a core-based algorithm...
A new self-adaptive improved differential evolution algorithm is presented. In order to improve the population's diversity and the ability of breaking away from the local optimum, according to the value of the variance of the population's fitness during the evolution process, a new mutation operator is adapted to mutate the population. In order to balance global and local search ability, the Scaling...
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