A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Abstract This paper deals with the decision rules of a tree classifier for performing the classification at each nonterminal node, under the assumption of complete probabilistic information. For given tree structure and feature subsets to be used, the optimal decision rules (strategy) are derived which minimize the overall probability of misclassification. The primary result is illustrated by an example. <s> BIB001 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Abstract Two different Artificial Neural Network (ANN) classifiers have been compared with a traditional closest-mean-classifier, a VQ and a Kohonen-network with respect to classification of vowels extracted from continuous speech. The first ANN-classifier is a standard three-layer perceptron network and the second ANN-classifier uses the response from a 16 × 16 Kohonen map as input to a two layer perceptron network. The result shows that the best performance is achieved with the two ANN-classifiers and indicates that a Kohonen map does not deteriorate the information presented to the second layer in the network and hence can be used instead of a first hidden layer. <s> BIB002 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms for a limited number of generations after a change occurred. Different methods for determining the time when to rejoin the hierarchy and how to handle the topmost sub-swarm are discussed. The test results show that H-PSO performs significantly better than PSO on all test functions and that the PH-PSO algorithms often perform best on multimodal functions where changes are not too severe. <s> BIB003 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. These approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and the amount of used memory. There is a trade off between these parameters. That is to say that optimization of one may cause obstruction in the other, hence all existing approaches try to establish equilibrium. In this study, considering the effect of the whole data set on class assigning of any data, we propose a new approach to construct not perfectly accurate, but less complex trees in a short time, using small amount of memory. To achieve this purpose, a multi-step process has been used. We trace the training data set twice in any step, from the beginning to the end and vice versa, to extract the class pattern for attribute selection. Using the selected attribute, we make new branches in the tree. After making branches, the selected attribute and some records of training data set are deleted at the end of any step. This process continues alternatively in several steps for remaining data and attributes until the tree is completely constructed. In order to have an optimized tree the parameters which we use in this algorithm are optimized using genetic algorithms. In order to compare this new approach with previous ones we used some known data sets which have been used in different researches. This approach has been compared with others based on the classification accuracy and also the decision tree size. Experimental results show that it is efficient to use this approach particularly in cases of massive data sets, memory restrictions or short learning time. <s> BIB004 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Data clustering is one of the most popular techniques in data mining. It is a method of grouping data into clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The most popular clustering algorithm K-mean and other classical algorithms suffer from disadvantages of initial centroid selection, local optima, low convergence rate problem etc. Particle Swarm Optimization (PSO) is a population based globalized search algorithm that mimics the capability (cognitive and social behavior) of swarms. PSO produces better results in complicated and multi-peak problems. This paper presents a literature survey on the PSO application in data clustering. PSO variants are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of PSO and data clustering. <s> BIB005 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Developing an efficient classification method is a challenge task in many research domains, such as neural network (NN) classifiers, statistical classifiers and machine learning. This study focuses on NN classifiers, which are data-driven analytical techniques. This study presents a cerebellar model articulation controller NN (CMAC NN) classifier, which has the advantages of very fast learning, reasonable generalization ability and robust noise resistance. To increase the accuracies of training and generalization, the CMAC NN classifier is designed with multiple-input and multiple-output (MIMO) network topology. The performance of the proposed MIMO CMAC NN classifier is evaluated using PROBEN1 benchmark datasets (such as for diabetes, cancer and glass) taken from the UCI Machine Learning Repository. Numerical results indicate that the proposed CMAC NN classifier is efficient for tested datasets. Moreover, this study compares the experimental results of the CMAC NN classifier with those in the published literature, indicating that the CMAC NN classifier is superior to some published classifiers. Therefore, the CMAC NN classifier can be considered as an analytical tool for solving classification tasks, such as medical decision making. <s> BIB006 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> Artificial Bee Colony (ABC) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm. Clustering analysis, used in many disciplines and applications, is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, ABC is used for data clustering on benchmark problems and the performance of ABC algorithm is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering. <s> BIB007 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> In recent years, heuristic algorithms have been successfully applied to solve clustering and classification problems. In this paper, gravitational search algorithm (GSA) which is one of the newest swarm based heuristic algorithms is used to provide a prototype classifier to face the classification of instances in multi-class data sets. The proposed method employs GSA as a global searcher to find the best positions of the representatives (prototypes). The proposed GSA-based classifier is used for data classification of some of the well-known benchmark sets. Its performance is compared with the artificial bee colony (ABC), the particle swarm optimization (PSO), and nine other classifiers from the literature. The experimental results of twelve data sets from UCI machine learning repository confirm that the GSA can successfully be applied as a classifier to classification problems. <s> BIB008 </s> A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification <s> VI. GSA IN CLASSIFICATION <s> There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. <s> BIB009
Classification is the process of mapping the objects from input space to set of predefined classes and allocate the objects with one of class from set of predefined classes. There are many classification techniques that have been developed and applied on the datasets to classify the objects such as decision tree BIB001 BIB004 , Artificial neural network BIB006 BIB002 , ABC BIB007 , PSO BIB005 , Support vector machine  etc. Accuracy is the crucial parameter in the classification problems to test the performance of the classification techniques whether the classification techniques properly classified the instance of a datasets or not. Due to diversity in the classification problems there does not exists a single technique that has been applied on all of the classification problems. This paper enlightens the application of GSA in the classification and provided the comparisons of GSA to other techniques. Hossein Askari and Seyed-Hamid Zahiri  have proposed new algorithm based on GSA for classification problem and called it Intelligent GSA i.e. IGSA classifier. In IGSA fuzzy rule is used to modify the value of two parameters: swarm size define the number of object and gravitational coefficient of GSA. The performance of IGSA classifier compares with IPS classifier ( PSO based classifier), IGA classifier (GA) and DE based classifier (Differential Equation based classifier) with accuracy, minimum, maximum and average score of recognition including five datasets from UCI repository in which IGSA provides better performance. Abbas Bahrololoum et al. BIB008 has proposed a new prototype classifier based on GSA for classification problem and three different fitness function defined for this classifier to evaluate the one representative for each class. Three version of GSA classifier has been developed with the help of three fitness functions. The performance of newly build classifiers compare with PSO and ABC including 12 datasets that are taken from the UCI repository and average misclassification percentage error parameter. To improve the classification accuracy for binary problems, Soroor Sarafrazi et al  has proposed a new hybrid algorithm GSA-SVM based on GSA & SVM. The performance of proposed algorithm evaluates with accuracy (standard deviation, mean, minimum & maximum) parameter using five datasets from UCI repository and compares with PSO-SVM & PSO-GA in which GSA-SVM provides better performance than others. To overcome parameter setting problem in SVM & increase the accuracy of feature subset selection Soroor Sarafrazi and Hossein Nezamabadi-pour  has proposed a new hybrid algorithm GSA-SVM based on original GSA, Binary GSA & SVM for binary problems in which original GSA is used to setting the parameters of SVM and Binary GSA for feature subset selection. The performance of GSA evaluates with accuracy parameter (Mean & Standard Deviation) using eight well known binary datasets from UCI repository and compares with PSO and GA-SVM in which GSA-SVM predicts data more accurately than others. Discussion: To the depth study of GSA in classification problem, it has been observed that there exist some common data sets on which GSA & its variants compare with other classification methods using some parameters and GSA & its variants provides better performance than others. Such datasets are Iris, wine, glass & cancer and performance of classification methods evaluate with misclassified instance, accuracy, rank parameters etc. Table 3 BIB009  BIB008  provides the comparisons of GSA & its variants (that are proposed for classification problems) with other techniques using above discussed common datasets with two parameters: accuracy & rank as well as some other methods also include such as ID3, J48, SVM, IBK etc. (11) 56 (14) 12 (1) 19 (5) 17 (4) 13 (2) (11) 53 (12) 55 (13) 55 (13) 56 BIB003 VII. CONCLUSION This paper presents the review of pervious research in the field of gravitational search algorithm, its variants; hybridize GSA and its applications. Table 1 provides the complete summary of GSA literature, modification in GSA and applications of GSA. From the table 1, it conclude that GSA is four years old but this field shows tremendous growth & quite popular between researchers and large number of problems solved by GSA such as RPDP, ED, OPF and many more mentioned in table 1. The figure 1 shows the percentage of hybrid algorithms and modification in GSA has been proposed by various authors in GSA literature. The modification and hybridization of GSA with other techniques provided better results in terms of computational time, convergence etc. The figure 3 provides the year wise development in the field of gravitational algorithms and number of publications in each year published with the help of GSA. From the figure 3, it's conclude that there is only one publication on GSA in year 2009 but in last two years large number of papers published using GSA that shows the wide popularity of GSA among researchers. The figure 4 categorizes the publications based on GSA in different domains. The figure 4 states that half of GSA publications related to computer science and computing filed that shows the GSA applicability and significance in computer science & computing field. The twenty four percent of GSA publications come from optimization & others field and very less publication from civil & mechanical field. From the figure 4 , it is conclude that GSA is widely used in computer science & computing field but a very less publications from civil & mechanical fields. Figure 4 exhibits that GSA is widely use in computer science & computing field but this field contains large number of subfields to pursue further research such as networks, security, ANN, pattern recognitions, clod computing, data mining etc. The figure 5 shows the applicability of GSA based publications in computer science & computing field. Figure 5 articulates that more than fifty percent publications of GSA in computer science & computing field belong to data mining domain. Data mining process defined by three terms i.e. classification, clustering and feature sub selection. The depth study of GSA shows that large number of The ability to find near global optimum solution makes the GSA differ from other nature inspired algorithms. But the GSA is also suffers from several problems such as computational time, convergence problem if initial population not generated well. But these problems of GSA removed by the hybridization of GSA. Another thing that must be point, there is no theoretical work on the computational time and convergence of GSA these work must be perform. In this review, it is notice that there are some other areas that are not touch by the GSA. Few of these are security application, analysis of time series data, applicability of the GSA in dynamic & uncertain environment, gene expressions, protein legend etc. 