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In real world, the datasets are having varying dimensions which incorporates noisy, irrelevant and redundant data which is hard to analyze. Feature selection is a preprocessing step used for selecting the significant information. The selection of optimal feature subset is an optimization problem which has been solved by several versions of metaheuristic algorithms. The metaheuristic optimization algorithm...
This paper presents a new hybrid HPSO-DE classification algorithm that combines the advantages of particle swarm optimization algorithm and differential evolution algorithm. Major improvements achieved by this combination are 1) flight improvement — flight behaviors are more and better diversified because each of the top 3 particles gets put into 3 different groups of the rest and then each group...
In this study, a decision support system has been developed for land mine detection and classification. Data obtained from detector based magnetic anomaly have been used to classify the land mines. With this classification, it is decided that whether obtained data belongs to a land mine or not, and the type of mine. The meta-heuristic k-NN classifier (HKC) has been used in developed decision support...
Discovering the effective subset of models in a pool of classifiers is an important and remarkable topic in ensemble learning scope. Using meticulously selected subset instead of entire ensemble leads to more efficient and effective results. This paper introduces a novel hybrid ensemble selection method of firefly and forward search algorithms. Because of the two different selection phases in the...
Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multi-population genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely...
Full Model Selection (FMS) selects the optimal amalgamation of pre-processing technique, feature subset and learning algorithm that obtains the least classification error for a given dataset. Meta-heuristic optimization algorithms are quite suitable for FMS, since it needs to explore and exploit a large solution space. This paper investigates the ability of an efficient meta-heuristic, named Bat algorithm...
In the literature, there are some studies which investigate if there is a relationship between fingerprint and gender or not. In these studies, this relationship is examined based on some vectorial parts of fingerprints. The main problem in these studies is the lack of data, depending on ethnical background and country, and there is not an exact finding of true classification results. It is known...
Classification is a supervised learning technique that predicts the classes of unobserved data by employing a model built from available data. One of the efficient ways to represent this predictive model is to express it as an optimal set of classification rules to provide comprehensibility and precision, simultaneously. In this paper, we propose a novel incremental parallel Particle Swarm Optimization...
Mitochondrial DNA has been used for studying population history and migration route of human populations through haplogroup analyses. Haplogroup is a gathering of same haplotypes, having same genetic mutations. It shows different distribution between each groups and difference of disease sensitivity depending on haplogroup has reported. Accurate haplogrouping is very important to population genetics...
Differential evolution (DE) is a promising algorithm for continuous optimization. Its two parameters, CR and F, have great effect on the algorithm performance. In recent years many DE algorithms with parameter control mechanisms were proposed. In this paper we propose a taxonomy to classify these algorithms according to the number of candidate parameter values, the number of parameter values used...
In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative...
This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification task of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3...
We describe in this paper an overview of artificial immune system algorithms to solve the classification problem in industrial monitoring. We present artificial immune system algorithms, starting with the negative selection that happens to be a rich source of inspiration. We also, detail the clonal selection algorithm, which is based on the clonal selection theory. Finally, we detail other algorithms...
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