To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ...To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.展开更多
Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit...Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.展开更多
To identify human thermal comfort in naturally ventilated buildings,the research based on both subjective and objective data was carried out in Chongqing,P. R. China. The characteristics of subjects' clothing regu...To identify human thermal comfort in naturally ventilated buildings,the research based on both subjective and objective data was carried out in Chongqing,P. R. China. The characteristics of subjects' clothing regulation function,changes of actual mean thermal comfort vote (AMV) varying with time and acceptable operative temperature in natural conditions were analyzed. In addition,the indicator actual mean vote-actual percentage dissatisfied (AMV-APD) was used to study the actual dissatisfaction with thermal environment. The results indicate that regulative ability by changing clothing under natural ventilated conditions is very significant but limited simultaneously,about 1.7 ℃ per 0.1 clo. Under naturally ventilated conditions,people may have an acceptable operative temperature of 16-28 ℃. Based on the AMV-APD,the actual minimum percentage dissatisfied can reach 4% at AMV of -0.36.展开更多
基金This project was supported by the National Basic Research Programof China (2001CB309403)
文摘To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.
基金supported by the National Natural Science Foundation of China(6177340561751312)the Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020123)。
文摘Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.
基金Projects(50838009, 50678179) supported by the National Natural Science Foundation of ChinaProjects(2006BAJ02A09, 2006BAJ02A13-4) supported by the National Key Technologies R & D Program of ChinaProject(200909A1001) supported by Chongqing University Postgraduates’ Innovative Team Building Project
文摘To identify human thermal comfort in naturally ventilated buildings,the research based on both subjective and objective data was carried out in Chongqing,P. R. China. The characteristics of subjects' clothing regulation function,changes of actual mean thermal comfort vote (AMV) varying with time and acceptable operative temperature in natural conditions were analyzed. In addition,the indicator actual mean vote-actual percentage dissatisfied (AMV-APD) was used to study the actual dissatisfaction with thermal environment. The results indicate that regulative ability by changing clothing under natural ventilated conditions is very significant but limited simultaneously,about 1.7 ℃ per 0.1 clo. Under naturally ventilated conditions,people may have an acceptable operative temperature of 16-28 ℃. Based on the AMV-APD,the actual minimum percentage dissatisfied can reach 4% at AMV of -0.36.
