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
prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s me...prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements.展开更多
In today＇s world of excessive development in technologies, sustainability and adaptability of computer applications is a challenge, and future prediction became significant. Therefore, strong artificial intelligence ...In today＇s world of excessive development in technologies, sustainability and adaptability of computer applications is a challenge, and future prediction became significant. Therefore, strong artificial intelligence （AI） became important and, thus, statistical machine learning （ML） methods were applied to serve it. These methods are very difficult to understand, and they predict the future without showing how. However, understanding of how machines make their decision is also important, especially in information system domain. Consequently, incremental covering algorithms （CA） can be used to produce simple rules to make difficult decisions. Nevertheless, even though using simple CA as the base of strong AI agent would be a novel idea but doing so with the methods available in CA is not possible. It was found that having to accurately update the discovered rules based on new information in CA is a challenge and needs extra attention. In specific, incomplete data with missing classes is inappropriately considered, whereby the speed and data size was also a concern, and future none existing classes were neglected. Consequently, this paper will introduce a novel algorithm called RULES-IT, in order to solve the problems of incremental CA and introduce it into strong AI. This algorithm is the first incremental algorithm in its family, and CA as a whole, that transfer rules of different domains to improve the performance, generalize the induction, take advantage of past experience in different domain, and make the learner more intelligent. It is also the first to introduce intelligent aspectsinto incremental CA, including consciousness, subjective emotions, awareness, and adjustment. Furthermore, all decisions made can be understood due to the simple representation of repository as rules. Finally, RULES-IT performance will be benchmarked with six different methods and compared with its predecessors to see the effect of transferring rules in the learning process, and to prove how RULES-IT actually solved the shortcoming of current incremental CA in addition to its improvement in the total performance.展开更多
In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high perf...In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high performance.In this paper,taking the MaxCut problem as our example,we introduce the idea of incremental learning into quantum computing,and propose a Quantum Proactive Incremental Learning algorithm(QPIL).Instead of a one-off training of quantum circuit,QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices,proactively reducing large-scale problems to smaller ones to solve in steps,providing an efficient solution for MaxCut.Specifically,some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first.Then,in each incremental phase,the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase.We perform experiments on 120 different small-scale graphs,and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR),time cost,anti-forgetting,and solv-ing stability.In particular,QPIL’s AR surpasses 20%of mainstream quantum baselines,and the time cost is less than 1/5 of them.The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale MaxCut and other combinatorial optimization problems.展开更多
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.
基金the National Natural Science Foundation of China (Grant No. 31400709 to X. C.)National Key Technology Support Program of China (Grant No. 2013BAK06B08)+1 种基金Scientific Research Fund of Zhejiang Provincial Education Department (China)(Grant No. Y201432207 to X. C.)Natural Science Fund of Jiangsu Province (China)(Grant No: BK20130187).
文摘prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements.
文摘In today＇s world of excessive development in technologies, sustainability and adaptability of computer applications is a challenge, and future prediction became significant. Therefore, strong artificial intelligence （AI） became important and, thus, statistical machine learning （ML） methods were applied to serve it. These methods are very difficult to understand, and they predict the future without showing how. However, understanding of how machines make their decision is also important, especially in information system domain. Consequently, incremental covering algorithms （CA） can be used to produce simple rules to make difficult decisions. Nevertheless, even though using simple CA as the base of strong AI agent would be a novel idea but doing so with the methods available in CA is not possible. It was found that having to accurately update the discovered rules based on new information in CA is a challenge and needs extra attention. In specific, incomplete data with missing classes is inappropriately considered, whereby the speed and data size was also a concern, and future none existing classes were neglected. Consequently, this paper will introduce a novel algorithm called RULES-IT, in order to solve the problems of incremental CA and introduce it into strong AI. This algorithm is the first incremental algorithm in its family, and CA as a whole, that transfer rules of different domains to improve the performance, generalize the induction, take advantage of past experience in different domain, and make the learner more intelligent. It is also the first to introduce intelligent aspectsinto incremental CA, including consciousness, subjective emotions, awareness, and adjustment. Furthermore, all decisions made can be understood due to the simple representation of repository as rules. Finally, RULES-IT performance will be benchmarked with six different methods and compared with its predecessors to see the effect of transferring rules in the learning process, and to prove how RULES-IT actually solved the shortcoming of current incremental CA in addition to its improvement in the total performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.62272048,61976056,and 62371069)BUPT Excellent Ph.D.Students Foundation(Grant No.CX20241055)。
文摘In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high performance.In this paper,taking the MaxCut problem as our example,we introduce the idea of incremental learning into quantum computing,and propose a Quantum Proactive Incremental Learning algorithm(QPIL).Instead of a one-off training of quantum circuit,QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices,proactively reducing large-scale problems to smaller ones to solve in steps,providing an efficient solution for MaxCut.Specifically,some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first.Then,in each incremental phase,the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase.We perform experiments on 120 different small-scale graphs,and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR),time cost,anti-forgetting,and solv-ing stability.In particular,QPIL’s AR surpasses 20%of mainstream quantum baselines,and the time cost is less than 1/5 of them.The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale MaxCut and other combinatorial optimization problems.
