The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,t...It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,the learning performance of attributes in derived reduct is much more crucial.Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct,those measures may have a direct impact on the performance of selected attributes in reduct.However,most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective,which are insufficient to identify attributes with superior learning performance,such as stability and accuracy.In order to improve the classification stability and classification accuracy of reduct,in this paper,a novel measure is proposed based on the fusion of supervised and unsupervised perspectives:(1)in terms of supervised perspective,approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels;(2)in terms of unsupervised perspective,conditional entropy is helpful in quantitatively describing the internal structure of data itself.In order to prove the effectiveness of the proposed measure,18 University of CaliforniaIrvine(UCI)datasets and 2 Yale face datasets have been employed in the comparative experiments.Finally,the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.展开更多
In recent years,medium entropy alloys have become a research hotspot due to their excellent physical and chemical performances.By controlling reasonable elemental composition and processing parameters,the medium entro...In recent years,medium entropy alloys have become a research hotspot due to their excellent physical and chemical performances.By controlling reasonable elemental composition and processing parameters,the medium entropy alloys can exhibit similar properties to high entropy alloys and have lower costs.In this paper,a FeCoNi medium entropy alloy precursor was prepared via sol-gel and coprecipitation methods,respectively,and FeCoNi medium entropy alloys were prepared by carbothermal and hydrogen reduction.The phases and magnetic properties of FeCoNi medium entropy alloy were investigated.Results showed that FeCoNi medium entropy alloy was produced by carbothermal and hydrogen reduction at 1500℃.Some carbon was detected in the FeCoNi medium entropy alloy prepared by carbothermal reduction.The alloy prepared by hydrogen reduction was uniform and showed a relatively high purity.Moreover,the hydrogen reduction product exhibited better saturation magnetization and lower coercivity.展开更多
Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducte...Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.展开更多
Value-at-Risk (VaR) estimation via Monte Carlo (MC) simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high ...Value-at-Risk (VaR) estimation via Monte Carlo (MC) simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high portfolio losses (more general risk measure) based on the Cross - Entropy importance sampling is developed. This algorithm can easily be applied in any light- or heavy-tailed case without an extra adaptation. Besides, it does not loose in the performance in comparison to other known methods. A numerical study in both cases is performed and the variance reduction rate is compared with other known methods. The problem of VaR estimation using procedures for estimating the probability of high portfolio losses is also discussed.展开更多
To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under...To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data.展开更多
High-entropy materials are composed of five or more metal elements with equimolar or near-equimolar concentrations within one crystal structure,which offer remarkable structural properties for many applications.Despit...High-entropy materials are composed of five or more metal elements with equimolar or near-equimolar concentrations within one crystal structure,which offer remarkable structural properties for many applications.Despite previously reported entropy-driven stabilization mechanisms,many high-entropy materials still tend to decompose to produce a variety of derivatives under operating conditions.In this study,we use transition-metal(Ni,Co,Ni,Zn,V)-based high-entropy metal-organic frameworks(HE-MOFs)as the precursors to produce different derivatives under acidic/alkaline treatment.We have shown that HE-MOFs and derivatives have shown favorable kinetics for N_(2)electrofixation in different pH electrolytes,specifically cathodic nitrogen reduction reaction in acidic media and anodic oxygen evolution reaction in alkaline media.To buffer the pH mismatch,we have further constructed an asymmetric acidic/alkaline device prototype by using bipolar membranes.As expected,the prototype showed remarkable activities,with an NH_(3)yield rate of 42.76μg h^(−1)mg^(−1),and Faradaic efficiency of 14.75%and energy efficiency of 2.59%,which are 14.4 and 4.4 times larger than those of its symmetric acidic and alkaline counterparts,respectively.展开更多
Partition and entropy of partitions in quantum logic are introduced and their properties are investigated.The results are generalized to the general case of T-norm and T-conorm.
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
基金supported by the National Natural Science Foundation of China(Grant Nos.62006099,62076111)the Key Research and Development Program of Zhenjiang-Social Development(Grant No.SH2018005)+1 种基金the Natural Science Foundation of Jiangsu Higher Education(Grant No.17KJB520007)Industry-school Cooperative Education Program of the Ministry of Education(Grant No.202101363034).
文摘It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,the learning performance of attributes in derived reduct is much more crucial.Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct,those measures may have a direct impact on the performance of selected attributes in reduct.However,most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective,which are insufficient to identify attributes with superior learning performance,such as stability and accuracy.In order to improve the classification stability and classification accuracy of reduct,in this paper,a novel measure is proposed based on the fusion of supervised and unsupervised perspectives:(1)in terms of supervised perspective,approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels;(2)in terms of unsupervised perspective,conditional entropy is helpful in quantitatively describing the internal structure of data itself.In order to prove the effectiveness of the proposed measure,18 University of CaliforniaIrvine(UCI)datasets and 2 Yale face datasets have been employed in the comparative experiments.Finally,the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.
基金financially supported by the National Natural Science Foundation of China(Nos.52074078 and 52374327)the Applied Fundamental Research Program of Liaoning Province,China(No.2023JH2/101600002)+3 种基金the Liaoning Provincial Natural Science Foundation,China(No.2022-YQ-09)the Shenyang Young Middle-Aged Scientific and Technological Innovation Talent Support Program,China(No.RC220491)the Liaoning Province Steel Industry-University-Research Innovation Alliance Cooperation Project of Bensteel Group,China(No.KJBLM202202)the Fundamental Research Funds for the Central Universities,China(Nos.N2201023 and N2325009)。
文摘In recent years,medium entropy alloys have become a research hotspot due to their excellent physical and chemical performances.By controlling reasonable elemental composition and processing parameters,the medium entropy alloys can exhibit similar properties to high entropy alloys and have lower costs.In this paper,a FeCoNi medium entropy alloy precursor was prepared via sol-gel and coprecipitation methods,respectively,and FeCoNi medium entropy alloys were prepared by carbothermal and hydrogen reduction.The phases and magnetic properties of FeCoNi medium entropy alloy were investigated.Results showed that FeCoNi medium entropy alloy was produced by carbothermal and hydrogen reduction at 1500℃.Some carbon was detected in the FeCoNi medium entropy alloy prepared by carbothermal reduction.The alloy prepared by hydrogen reduction was uniform and showed a relatively high purity.Moreover,the hydrogen reduction product exhibited better saturation magnetization and lower coercivity.
基金Climbing Peak Discipline Project of Shanghai Dianji University,China(No.15DFXK02)Hi-Tech Research and Development Programs of China(No.2007AA041600)
文摘Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.
文摘Value-at-Risk (VaR) estimation via Monte Carlo (MC) simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high portfolio losses (more general risk measure) based on the Cross - Entropy importance sampling is developed. This algorithm can easily be applied in any light- or heavy-tailed case without an extra adaptation. Besides, it does not loose in the performance in comparison to other known methods. A numerical study in both cases is performed and the variance reduction rate is compared with other known methods. The problem of VaR estimation using procedures for estimating the probability of high portfolio losses is also discussed.
基金Supported by the National Natural Science Foundation of China(No. 60573075), the National High Technology Research and Development Program of China (No. 2003AA133060) and the Natural Science Foundation of Shanxi Province (No. 200601104).
文摘To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data.
基金Fundamental Research Funds for the Central Universities,Grant/Award Numbers:30920041113,30921013103Natural Science Foundation of Jiangsu Province,Grant/Award Number:BK20190460+2 种基金Jiangsu innovative/entre‐preneurial talent program,Grant/Award Number:2019Basic Science Center Program for Ordered Energy Conversion of the National Natural Science Foundation of China,Grant/Award Number:51888103National Natural Science Foundation of China,Grant/Award Numbers:52006105,92163124。
文摘High-entropy materials are composed of five or more metal elements with equimolar or near-equimolar concentrations within one crystal structure,which offer remarkable structural properties for many applications.Despite previously reported entropy-driven stabilization mechanisms,many high-entropy materials still tend to decompose to produce a variety of derivatives under operating conditions.In this study,we use transition-metal(Ni,Co,Ni,Zn,V)-based high-entropy metal-organic frameworks(HE-MOFs)as the precursors to produce different derivatives under acidic/alkaline treatment.We have shown that HE-MOFs and derivatives have shown favorable kinetics for N_(2)electrofixation in different pH electrolytes,specifically cathodic nitrogen reduction reaction in acidic media and anodic oxygen evolution reaction in alkaline media.To buffer the pH mismatch,we have further constructed an asymmetric acidic/alkaline device prototype by using bipolar membranes.As expected,the prototype showed remarkable activities,with an NH_(3)yield rate of 42.76μg h^(−1)mg^(−1),and Faradaic efficiency of 14.75%and energy efficiency of 2.59%,which are 14.4 and 4.4 times larger than those of its symmetric acidic and alkaline counterparts,respectively.
文摘Partition and entropy of partitions in quantum logic are introduced and their properties are investigated.The results are generalized to the general case of T-norm and T-conorm.