Recommender system is a tool to suggest items to the users from the extensive history of the user’s feedback.Though,it is an emerging research area concerning academics and industries,where it suffers from sparsity,s...Recommender system is a tool to suggest items to the users from the extensive history of the user’s feedback.Though,it is an emerging research area concerning academics and industries,where it suffers from sparsity,scalability,and cold start problems.This paper addresses sparsity,and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations.In this paper,an effective movie recommendation system is proposed by Classification and Regression Tree(CART)algorithm,enhanced Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)algorithm and truncation method.In this research paper,a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process,where the proposed algorithm is named as enhanced BIRCH.The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage.In this paper,the proposed model is tested on Movielens dataset,and the performance is evaluated by means of Mean Absolute Error(MAE),precision,recall and f-measure.The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models.The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets.Further,the proposed model obtained 0.83 of precision,0.86 of recall and 0.86 of f-measure on Movielens 100k dataset,which are effective compared to the existing models in movie recommendation.展开更多
CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral ...In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.展开更多
针对图像单一特征分割结果的适应局限性,提出融合多特征和谱聚类集成的图像分割方法(MFSC-IS).首先对图像进行基于粒计算的多特征子分割;然后将分割结果映射到超图,利用谱聚类集成算法得到最终分割结果.实验结果表明,与Gpb(Globalized p...针对图像单一特征分割结果的适应局限性,提出融合多特征和谱聚类集成的图像分割方法(MFSC-IS).首先对图像进行基于粒计算的多特征子分割;然后将分割结果映射到超图,利用谱聚类集成算法得到最终分割结果.实验结果表明,与Gpb(Globalized probability of boundary)算法相比,融合多特征和谱聚类集成方法可以得到一个相对较好的分割结果.展开更多
文摘Recommender system is a tool to suggest items to the users from the extensive history of the user’s feedback.Though,it is an emerging research area concerning academics and industries,where it suffers from sparsity,scalability,and cold start problems.This paper addresses sparsity,and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations.In this paper,an effective movie recommendation system is proposed by Classification and Regression Tree(CART)algorithm,enhanced Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)algorithm and truncation method.In this research paper,a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process,where the proposed algorithm is named as enhanced BIRCH.The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage.In this paper,the proposed model is tested on Movielens dataset,and the performance is evaluated by means of Mean Absolute Error(MAE),precision,recall and f-measure.The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models.The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets.Further,the proposed model obtained 0.83 of precision,0.86 of recall and 0.86 of f-measure on Movielens 100k dataset,which are effective compared to the existing models in movie recommendation.
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
基金Supported by the National Natural Science Foundation of China (60661003)the Research Project Department of Education of Jiangxi Province (GJJ10566)
文摘In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.
文摘针对图像单一特征分割结果的适应局限性,提出融合多特征和谱聚类集成的图像分割方法(MFSC-IS).首先对图像进行基于粒计算的多特征子分割;然后将分割结果映射到超图,利用谱聚类集成算法得到最终分割结果.实验结果表明,与Gpb(Globalized probability of boundary)算法相比,融合多特征和谱聚类集成方法可以得到一个相对较好的分割结果.
