M. Rezaei; H. Nezamabadi-pour
Abstract
The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, ...
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The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, using the gravitational search algorithm (GSA), two hybrid schemes have been proposed in which PG and PS problems have been considered together. To evaluate the classification performance of these hybrid models, we have performed a comparative experimental study including a comparison between our proposals and some approaches previously studied in the literature using several benchmark datasets. The experimental results demonstrate that our hybrid approaches outperform most of the competitive methods.
H.R. Koosha; Z. Ghorbani; R. Nikfetrat
Abstract
In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are ...
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In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are of great importance, especially in today's hectic world, which search for content that would be interesting to an individual. In this research, a new two-steps recommender system is proposed based on demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naïve Bayesian Classification. The results are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results showed the effectiveness of the K-means/FA/KNN compared with other extant models.
Z. Hassani; M. Alambardar Meybodi
Abstract
A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses ...
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A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses a local search strategy by employing the Ant-Lion algorithm to select the less correlated and salient feature subset. The objective is to improve the prediction accuracy and adaptability of the model in various datasets by balancing the exploration and exploitation processes. The performance of our method has been evaluated on 30 benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets. To verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, MPSO, are elected to be compared with the efficiency of H-PSO-ALO. Considering the experimental results, the proposed method outperforms the others in many cases, so it seems it is a desirable candidate for optimization problems on real-world datasets.
