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- 저자 : ( Behrouze Minaei-bidgoli ) (검색결과 3 건)
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A Novel Feature Selection Method in the Categorization of Imbalanced Textual Data
( Jafar Pouramini ),( Behrouze Minaei-bidgoli ),( Mahdi Esmaeili ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.8
Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.
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Prediction of Stock Market Using an Ensemble Learning-based Intelligent Model
Mohammad-Taghi Faghihi-Nezhad,Behrouz Minaei-Bidgoli 대한산업공학회 2018 Industrial Engineeering & Management Systems Vol.17 No.3
AI-based models have shown that stock market is predictable despite its uncertainty and fluctuating nature. Research in this field has further dealt with predicting the next step price amount and less attention has been paid to the prediction of the next movement of price. However, in practice, the necessary requisite for decision-making and use of the results of prediction lies in considering the predictable trend of stock movement along with predicting stock price. Considering the widespread search in the literature on the matter, this paper takes into account, for the first time, two criteria of direction and price simultaneously for the prediction of the stock price. The proposed model has two stages and is developed based on ensemble learning and meta-heuristic optimization algorithms. The first stage predicts the direction of the next price movement. At the second stage, such prediction and other input variables create a new training dataset and the stock price is predicted. At each stage, in order to optimize the results, genetic algorithm (GA) optimization and particle swarm optimization (PSO) are applied. Evaluation of the results, on the real data of stock price, indicates that the proposed model has higher accuracy than other models used in the literature.
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A Probabilistic Algorithm for MANET Clustering
Fahimeh Dabaghi-Zarandi,Behrouz Minaei-Bidgoli,Zohreh Davarzani 보안공학연구지원센터 2014 International Journal of Future Generation Communi Vol.7 No.6
Mobile ad hoc network (MANET) is a type of ad hoc network that MANET nodes can change their locations and configure by themselves on the fly. Because of mobility the MANET nodes, the management of a large MANET is difficult, therefore, clustering in a MANET is an important technique. A large network is divided into several sub networks applying clustering method. When the topology of the network is dynamic and ad hoc, the process of clustering is very complicated. In this paper, we propose a Probabilistic Algorithm for MANET Clustering (PAMC) to improve the performance of this wireless technology. We simulate our algorithm and evaluate it based on two criteria: the average number of clusters and the average re-affiliation.
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