2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), 2014
Swarm intelligence algorithms have been successfully applied as optimization tools in various app... more Swarm intelligence algorithms have been successfully applied as optimization tools in various applications, such as biology, commerce, and engineering. This paper presents BRADO (BRAin Drain Optimization) algorithm as a new socio-inspired swarm intelligence approach, in which the search algorithm is inspired by the process of brain drain phenomenon. In order to evaluate the BRADO performance, it was applied to several benchmark optimization functions and the results produced by BRADO, particle swarm optimization, imperialist competitive algorithm and GA have been compared. Our findings show the BRADO superiority to avoid the regions around local minima and dealing with high dimensionality problems.
Rule-based classification is one of the most important topics in the field of data mining due to ... more Rule-based classification is one of the most important topics in the field of data mining due to its wide applications. This article presents a novel rule-based classifier called RACER (Rule Aggregating ClassifiER) to improve the accuracy of data classification. RACER uses a specific rule representation that enables it to consider each instance in the training data as an initial rule, without spending any cost. In order to retrieve an applicable rule set, RACER tries to combine the initial rules together. If the combined rule has a better fitness value in comparison with the two input rules, RACER combines them together. We have used seventeen different datasets from UCI machine learning database repository to evaluate RACER’s capability in classifying various kinds of databases. Moreover, to assess RACER’s performance, we compared our results with some other well-known classifiers including CN.2, PART, C4.5 and SVM. Our experiments show that RACER is an effective classifier in various domains and has better average classification accuracy and understandability in comparison with other applied classifiers.
In the area of professional social networks, collaborative team formation with its NP-hard nature... more In the area of professional social networks, collaborative team formation with its NP-hard nature, has attracted the attention of many researchers. The purpose of this study is to find a collaborative team which covers required skills and minimizes the communication cost among team members. To solve this problem, BRADO (BRAin Drain Optimization), a recently-proposed meta-heuristic swarm-based algorithm which simulates the brain drain phenomenon, has been utilized. In order to evaluate BRADO, it has been applied in extensive experiments to the DBLP and IMDb datasets. Results demonstrate the effectiveness and superiority of the BRADO algorithm in comparison with PSO, GA, ICA, RarestFirst and EnhancedSteiner algorithms. Our findings lead us to believe that the BRADO algorithm can be a promising method in the context of team formation problem.
Journal of Parallel and Distributed Computing, 2015
ABSTRACT Minimizing energy consumption is the most important concern in wireless sensor networks ... more ABSTRACT Minimizing energy consumption is the most important concern in wireless sensor networks (WSNs). To achieve this, clustering and prediction methods can enjoy the inherent redundancy of raw data and reduce transmissions more effectively. In this paper, we focus on designing a prediction-based approach, named PDC, to mainly contribute in data-aware clustering. It exploits both spatial and temporal correlations to form highly stable clusters of nodes sensing similar values. Here, the sink node uses only the local prediction models of cluster heads to forecast all readings in the network without direct communication. To the best of our knowledge, PDC is a novel energy efficient approach which provides a high precision of the approximate results with bounded error. Our simple prediction model presents high accuracy as well as low computation and communication costs. Extensive simulations have been conducted to evaluate the prediction model as well as our clustering approach. The results verify the superiority of our simple prediction model. Moreover, PDC implies a significant improvement on existing alternatives.
6th International Symposium on Telecommunications (IST), 2012
ABSTRACT Acquiring new customers in any business is much more costly than trying to retain the ex... more ABSTRACT Acquiring new customers in any business is much more costly than trying to retain the existing ones. So, many prediction methods have been suggested to detect churning customers. In this paper, the CORER (Colonial cOmpetitive Rule-based classifiER) classification algorithm is brought to the attention of marketing researchers to enhance the prediction accuracy of existing churn management systems. CORER is new rule-based classifier which works based on Imperialist Competitive Algorithm (ICA), a recently-proposed evolutionary optimization algorithm. Applied to the database of a telecommunication company, this classifier is found to remarkably improve accuracy in predicting churn in comparison with the most well-known techniques in the literature of the churn management, namely LOLIMOT, C5.0, neural networks and boosting classification trees. Our findings lead us to believe that the CORER classifier could cause to increase profit for the companies.
2010 5th International Symposium on Telecommunications, 2010
The continued growth of Email usage, which is naturally followed by an increase in unsolicited em... more The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area. In the context of spam filtering systems, addressing the evolving nature of spams, which leads to obsolete the related models, has been always a challenge. In this paper an adaptive spam filtering system based on language model is proposed which can detect concept drift based on computing the deviation in email contents distribution. The proposed method can be used along with any existing classifier; particularly in this paper we use Naïve Bayes method as classifier. The proposed method has been evaluated with Enron data set. The results indicate the efficiency of the method in detecting concept drift and its superiority over Naïve Bayes classifier in terms of accuracy.
2010 5th International Symposium on Telecommunications, 2010
Recommender systems have become significant tools in electronic commerce, proposing effectively t... more Recommender systems have become significant tools in electronic commerce, proposing effectively those items that best meet the preferences of users. A variety of techniques have been proposed for the recommender systems such as, collaborative filtering and content-based filtering. This study proposes a new hybrid recommender system that focuses on improving the performance under the “new user cold-start” condition where existence
ABSTRACT Economical crisis in recent years leads the banks to pay more attention to credit risk a... more ABSTRACT Economical crisis in recent years leads the banks to pay more attention to credit risk assessment. Financial institutes have used various kinds of decision support systems, to reduce their credit risk. Credit scoring is one of the most important systems that have been used by the banks and financial institutes. In this paper, an application of locally linear model tree (LOLIMOT) algorithm was experimented to improve the predictive accuracy of credit scoring. Using the Australian credit data from UCI machine learning database repository, the algorithm was found an increase in predictive accuracy in comparison with some other well-known methods in the credit scoring area. The experiments also indicate that LOLIMOT get the best result in terms of average accuracy and type I and II error.
2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), 2014
Swarm intelligence algorithms have been successfully applied as optimization tools in various app... more Swarm intelligence algorithms have been successfully applied as optimization tools in various applications, such as biology, commerce, and engineering. This paper presents BRADO (BRAin Drain Optimization) algorithm as a new socio-inspired swarm intelligence approach, in which the search algorithm is inspired by the process of brain drain phenomenon. In order to evaluate the BRADO performance, it was applied to several benchmark optimization functions and the results produced by BRADO, particle swarm optimization, imperialist competitive algorithm and GA have been compared. Our findings show the BRADO superiority to avoid the regions around local minima and dealing with high dimensionality problems.
Rule-based classification is one of the most important topics in the field of data mining due to ... more Rule-based classification is one of the most important topics in the field of data mining due to its wide applications. This article presents a novel rule-based classifier called RACER (Rule Aggregating ClassifiER) to improve the accuracy of data classification. RACER uses a specific rule representation that enables it to consider each instance in the training data as an initial rule, without spending any cost. In order to retrieve an applicable rule set, RACER tries to combine the initial rules together. If the combined rule has a better fitness value in comparison with the two input rules, RACER combines them together. We have used seventeen different datasets from UCI machine learning database repository to evaluate RACER’s capability in classifying various kinds of databases. Moreover, to assess RACER’s performance, we compared our results with some other well-known classifiers including CN.2, PART, C4.5 and SVM. Our experiments show that RACER is an effective classifier in various domains and has better average classification accuracy and understandability in comparison with other applied classifiers.
In the area of professional social networks, collaborative team formation with its NP-hard nature... more In the area of professional social networks, collaborative team formation with its NP-hard nature, has attracted the attention of many researchers. The purpose of this study is to find a collaborative team which covers required skills and minimizes the communication cost among team members. To solve this problem, BRADO (BRAin Drain Optimization), a recently-proposed meta-heuristic swarm-based algorithm which simulates the brain drain phenomenon, has been utilized. In order to evaluate BRADO, it has been applied in extensive experiments to the DBLP and IMDb datasets. Results demonstrate the effectiveness and superiority of the BRADO algorithm in comparison with PSO, GA, ICA, RarestFirst and EnhancedSteiner algorithms. Our findings lead us to believe that the BRADO algorithm can be a promising method in the context of team formation problem.
Journal of Parallel and Distributed Computing, 2015
ABSTRACT Minimizing energy consumption is the most important concern in wireless sensor networks ... more ABSTRACT Minimizing energy consumption is the most important concern in wireless sensor networks (WSNs). To achieve this, clustering and prediction methods can enjoy the inherent redundancy of raw data and reduce transmissions more effectively. In this paper, we focus on designing a prediction-based approach, named PDC, to mainly contribute in data-aware clustering. It exploits both spatial and temporal correlations to form highly stable clusters of nodes sensing similar values. Here, the sink node uses only the local prediction models of cluster heads to forecast all readings in the network without direct communication. To the best of our knowledge, PDC is a novel energy efficient approach which provides a high precision of the approximate results with bounded error. Our simple prediction model presents high accuracy as well as low computation and communication costs. Extensive simulations have been conducted to evaluate the prediction model as well as our clustering approach. The results verify the superiority of our simple prediction model. Moreover, PDC implies a significant improvement on existing alternatives.
6th International Symposium on Telecommunications (IST), 2012
ABSTRACT Acquiring new customers in any business is much more costly than trying to retain the ex... more ABSTRACT Acquiring new customers in any business is much more costly than trying to retain the existing ones. So, many prediction methods have been suggested to detect churning customers. In this paper, the CORER (Colonial cOmpetitive Rule-based classifiER) classification algorithm is brought to the attention of marketing researchers to enhance the prediction accuracy of existing churn management systems. CORER is new rule-based classifier which works based on Imperialist Competitive Algorithm (ICA), a recently-proposed evolutionary optimization algorithm. Applied to the database of a telecommunication company, this classifier is found to remarkably improve accuracy in predicting churn in comparison with the most well-known techniques in the literature of the churn management, namely LOLIMOT, C5.0, neural networks and boosting classification trees. Our findings lead us to believe that the CORER classifier could cause to increase profit for the companies.
2010 5th International Symposium on Telecommunications, 2010
The continued growth of Email usage, which is naturally followed by an increase in unsolicited em... more The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area. In the context of spam filtering systems, addressing the evolving nature of spams, which leads to obsolete the related models, has been always a challenge. In this paper an adaptive spam filtering system based on language model is proposed which can detect concept drift based on computing the deviation in email contents distribution. The proposed method can be used along with any existing classifier; particularly in this paper we use Naïve Bayes method as classifier. The proposed method has been evaluated with Enron data set. The results indicate the efficiency of the method in detecting concept drift and its superiority over Naïve Bayes classifier in terms of accuracy.
2010 5th International Symposium on Telecommunications, 2010
Recommender systems have become significant tools in electronic commerce, proposing effectively t... more Recommender systems have become significant tools in electronic commerce, proposing effectively those items that best meet the preferences of users. A variety of techniques have been proposed for the recommender systems such as, collaborative filtering and content-based filtering. This study proposes a new hybrid recommender system that focuses on improving the performance under the “new user cold-start” condition where existence
ABSTRACT Economical crisis in recent years leads the banks to pay more attention to credit risk a... more ABSTRACT Economical crisis in recent years leads the banks to pay more attention to credit risk assessment. Financial institutes have used various kinds of decision support systems, to reduce their credit risk. Credit scoring is one of the most important systems that have been used by the banks and financial institutes. In this paper, an application of locally linear model tree (LOLIMOT) algorithm was experimented to improve the predictive accuracy of credit scoring. Using the Australian credit data from UCI machine learning database repository, the algorithm was found an increase in predictive accuracy in comparison with some other well-known methods in the credit scoring area. The experiments also indicate that LOLIMOT get the best result in terms of average accuracy and type I and II error.
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