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- MakaleAçık Erişim2021Scopus SJRQ1A novel approach for efficient stance detection in online social networks with metaheuristic optimization
Can, Umit; Alatas, Bilal
In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.
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İncele - Derleme MakaleAçık Erişim2019Scopus SJRQ2A new direction in social network analysis: Online social network analysis problems and applications
Can, Umit; Alatas, B.
The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.
AtıfScopus172WoS113İncele - MakaleAçık Erişim2017Scopus SJRQ3Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm
Can, Umit; Alatas, Bilal
The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.
AtıfScopus27WoS18OpenAlex28İncele - BildiriYalnız künye2016Predicting Links in Weighted Disease Networks
Gül, Serpil; Kaya, Mehmet; Kaya, Buket
This paper proposes a link prediction method to estimate what sort of disease risks may have been carried by the patients via surveying their and similar patients' medical histories. It is possible to make future oriented predictions with link prediction by deriving new data within a network structure. It is attempted to estimate the future structure of the network and the relations which will be created or abandoned by the individuals on the basis of the nodes within the network structure and the relations between these nodes. For this purpose, an undirected weighted disease network is first created by using the data of patients which were subjected to Hemogram test at F. rat University Hospital during 2013. In the disease network, each node represents a disease and the links represent a relationship between diseases. By the developed method, then disease risk prediction is made through a proactive approach by determining what sorts of disease risks were carried by the patients applying with certain complaints. Experimental results demonstrate the applicability of the link prediction in these networks.
AtıfWoS11İncele - MakaleAçık Erişim2016Scopus SJRQ2Automatic mining of accurate and comprehensible numerical classification rules with cat swarm optimization algorithm
Akyol, Sinem; Alatas, Bilal
Optimization is the process of finding the best solution of a problem. There are many optimization algorithms proposed for optimization problems. The metaheuristic algorithm can give solutions close to optimum in an acceptable period of time for large-scaled optimization problems. The metaheuristic optimization algorithms are evaluated in seven different groups which are biology-based, physics-based, swarm-based, social-based, music-based, sport-based, and chemistry-based. The swarm-based optimization algorithms have been developed by observing the behaviors of creatures e.g. birds, fishes, cats, bees etc. Data mining is the process of discovery of meaningful and useful data within huge databases. Classification rules mining is one of the most commonly studied data mining problems and with methods for this problem, users can easily understand the rules extracted from databases. In this work, one of the most recent swarm based optimization algorithm, Cat Swarm Optimization (CSO), has been firstly used for classification rules mining within databases composed of numerical or mixed data types automatically. There is not any preprocess for finding true ranges for appropriate attributes of the rules, this has been automatically done by CSO. Furthermore, the used objective function is very flexible and many different objectives can easily be added to. For this purpose, four numerical databases obtained from UCI data warehouse have been used and accurate and comprehensible classification rules have been mined. The results have been compared with the results obtained from Weka program. Although CSO has not been embedded with any improvement and has firstly implemented in this research area, the obtained results seem promising.
AtıfWoS9OpenAlex4İncele - MakaleAçık Erişim2015Scopus SJRQ3Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm
Kiziloluk, Soner; Alatas, Bilal
In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets.
AtıfWoS12OpenAlex18İncele - MakaleAçık Erişim2015Scopus SJRQ2Automatic classification of harmonic data using k-means and least square support vector machine
Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.
AtıfWoS1OpenAlex3İncele - MakaleYalnız künye2012Scopus SJRQ1A novel chemistry based metaheuristic optimization method for mining of classification rules
Alatas, Bilal
When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as artificial chemical reaction optimization algorithm, ACROA. In this study, one of the first applications of this method has been performed in classification rule discovery field of data mining and efficiency has been demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.
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