zulfiqar ali | National University of Computer and Emerging Sciences (original) (raw)
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ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2018
Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit pri... more Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution. , such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which inc...
In this paper, a novel approach for discovering emerging patterns has been proposed. Majority of ... more In this paper, a novel approach for discovering emerging patterns has been proposed. Majority of the existing algorithms for the discovery of emerging patterns are tree-based which involve growth and shrinking of trees for this purpose. These algorithms follow greedy search approach for discovery of emerging patterns. The proposed approach utilizes the diversity of ant colony optimization and avoids complexity and greedy search of tree-based algorithms for discovery of emerging patterns. The experiments show that the proposed approach provides higher accuracy than existing state of the art classifiers as well as emerging pattern-based classifiers.
— In this article we investigate the effect of discretization Methods on the Performance of Assoc... more — In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. and Chi2-D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.
In this article we investigate the effect of discretization Methods on the Performance of Associa... more In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. 1RD, AmevaD, BayesianD, CACCD, CADDD, DIBD D, ClusterAnalysisD, ChiMergeD and Chi2D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.
— In this article we investigate the effect of discretization Methods on the Performance of Assoc... more — In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. and Chi2-D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2018
Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit pri... more Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution. , such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which inc...
In this paper, a novel approach for discovering emerging patterns has been proposed. Majority of ... more In this paper, a novel approach for discovering emerging patterns has been proposed. Majority of the existing algorithms for the discovery of emerging patterns are tree-based which involve growth and shrinking of trees for this purpose. These algorithms follow greedy search approach for discovery of emerging patterns. The proposed approach utilizes the diversity of ant colony optimization and avoids complexity and greedy search of tree-based algorithms for discovery of emerging patterns. The experiments show that the proposed approach provides higher accuracy than existing state of the art classifiers as well as emerging pattern-based classifiers.
— In this article we investigate the effect of discretization Methods on the Performance of Assoc... more — In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. and Chi2-D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.
In this article we investigate the effect of discretization Methods on the Performance of Associa... more In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. 1RD, AmevaD, BayesianD, CACCD, CADDD, DIBD D, ClusterAnalysisD, ChiMergeD and Chi2D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.
— In this article we investigate the effect of discretization Methods on the Performance of Assoc... more — In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. and Chi2-D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.