Browsing by Author "Gupta, U."

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Glowworm swarm based informative attribute selection using support vector machines for simultaneous feature selection and classification
(2015) Gurav, A.; Nair, V.; Gupta, U.; Valadi, J.
In this paper, we propose a hybrid filter-wrapper algorithm, GSO-Infogain, for simultaneous feature selection for improved classification accuracy. GSO-Infogain employs Glowworm-Swarm Optimization(GSO) algorithm with Support Vector Machine(SVM) as its internal learning algorithm and utilizes feature ranking based on information gain as a heuristic. The GSO algorithm randomly generates a population of worms, each of which is a candidate subset of features. The fitness of each candidate solution, which is evaluated using Support Vector Machine, is encoded within its luciferin value. Each worm probabilistically moves towards the worm with the highest luciferin value in its neighbourhood. In the process, they explore the feature space and eventually converge to the global optimum. We have evaluated the performance of the hybrid algorithm for feature selection on a set of cancer datasets. We obtain a classification accuracy in the range 94-98% for these datasets, which is comparable to the best results from other classification algorithms. We further tested the robustness of GSO-Infogain by evaluating its performance on the CoEPrA training and test datasets. GSO-Infogain performs well in this experiment too by giving similar prediction accuracies on the training and test datasets thus indicating its robustness. � Springer International Publishing Switzerland 2015.
Glowworm swarm based informative attribute selection using support vector machines for simultaneous feature selection and classification
(Springer Verlag service@springer.de, 2015) Gurav, A.; Nair, V.; Gupta, U.; Jayaraman, J.
In this paper, we propose a hybrid filter-wrapper algorithm, GSO-Infogain, for simultaneous feature selection for improved classification accuracy. GSO-Infogain employs Glowworm-Swarm Optimization(GSO) algorithm with Support Vector Machine(SVM) as its internal learning algorithm and utilizes feature ranking based on information gain as a heuristic. The GSO algorithm randomly generates a population of worms, each of which is a candidate subset of features. The fitness of each candidate solution, which is evaluated using Support Vector Machine, is encoded within its luciferin value. Each worm probabilistically moves towards the worm with the highest luciferin value in its neighbourhood. In the process, they explore the feature space and eventually converge to the global optimum. We have evaluated the performance of the hybrid algorithm for feature selection on a set of cancer datasets. We obtain a classification accuracy in the range 94-98% for these datasets, which is comparable to the best results from other classification algorithms. We further tested the robustness of GSO-Infogain by evaluating its performance on the CoEPrA training and test datasets. GSO-Infogain performs well in this experiment too by giving similar prediction accuracies on the training and test datasets thus indicating its robustness. Â© Springer International Publishing Switzerland 2015.
Recommender system based on Hierarchical Clustering algorithm Chameleon
(2015) Gupta, U.; Patil, N.
Recommender Systems are becoming inherent part of today's e-commerce applications. Since recommender system has a direct impact on the sales of many products therefore Recommender system plays an important role in e-commerce. Collaborative filtering is the oldest techniques used in the recommender system. A lot of work has been done towards the improvement of collaborative filtering which comprises of two components User Based and Item Based. The basic necessity of today's recommender system is accuracy and speed. In this work an efficient technique for recommender system based on Hierarchical Clustering is proposed. The user or item specific information is grouped into a set of clusters using Chameleon Hierarchical clustering algorithm. Further voting system is used to predict the rating of a particular item. In order to evaluate the performance of Chameleon based recommender system, it is compared with existing technique based on K-means clustering algorithm. The results demonstrates that Chameleon based Recommender system produces less error as compared to K-means based Recommender System. � 2015 IEEE.
Recommender system based on Hierarchical Clustering algorithm Chameleon
(Institute of Electrical and Electronics Engineers Inc., 2015) Gupta, U.; Patil, N.
Recommender Systems are becoming inherent part of today's e-commerce applications. Since recommender system has a direct impact on the sales of many products therefore Recommender system plays an important role in e-commerce. Collaborative filtering is the oldest techniques used in the recommender system. A lot of work has been done towards the improvement of collaborative filtering which comprises of two components User Based and Item Based. The basic necessity of today's recommender system is accuracy and speed. In this work an efficient technique for recommender system based on Hierarchical Clustering is proposed. The user or item specific information is grouped into a set of clusters using Chameleon Hierarchical clustering algorithm. Further voting system is used to predict the rating of a particular item. In order to evaluate the performance of Chameleon based recommender system, it is compared with existing technique based on K-means clustering algorithm. The results demonstrates that Chameleon based Recommender system produces less error as compared to K-means based Recommender System. Â© 2015 IEEE.