Rough Set is a valid mathematical theory developed in recent years, which has been applied successfully in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring. In t...Rough Set is a valid mathematical theory developed in recent years, which has been applied successfully in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring. In this paper, the authors discuss the reduction of knowledge using conditional entropy in rough set theory. First, the changing tendency of the conditional entropy of decision attributes giving condition attributes is studied from the viewpoint of information. Next, a new reduction algorithm based on conditional entropy is developed. Furthermore, our simulation results show that the algorithm can find the minimal reduction in most cases.展开更多
In this paper,we introduce a novel hybrid variational model which generalizes the classical total variation method and the wavelet shrinkage method.An alternating minimization direction algorithm is then employed.We a...In this paper,we introduce a novel hybrid variational model which generalizes the classical total variation method and the wavelet shrinkage method.An alternating minimization direction algorithm is then employed.We also prove that it converges strongly to the minimizer of the proposed hybrid model.Finally,some numerical examples illustrate clearly that the new model outperforms the standard total variation method and wavelet shrinkage method as it recovers better image details and avoids the Gibbs oscillations.展开更多
Initiative-learning algorithms are characterized by and hence advantageousfor their independence of prior domain knowledge.Usually,their induced results could moreobjectively express the potential characteristics and ...Initiative-learning algorithms are characterized by and hence advantageousfor their independence of prior domain knowledge.Usually,their induced results could moreobjectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty,becauseuncertainty is an intrinsic common feature of and also an essential link between information systemsand their induced results.Obviously,the effectiveness of such initiative-learning framework isheavily dependent on the accuracy of system uncertainty measurements.Herein,a more reasonablemethod for measuring system uncertainty is developed based on rough set theory and the conception ofinformation entropy;then a new algorithm is developed on the bases of the new system uncertaintymeasurement and the Skowron's algorithm for mining prepositional default decision rules.Theproposed algorithm is typically initiative-learning.It is well adaptable to system uncertainty.Asshown by simulation experiments,its comprehensive performances are much better than those ofcongeneric algorithms.展开更多
文摘Rough Set is a valid mathematical theory developed in recent years, which has been applied successfully in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring. In this paper, the authors discuss the reduction of knowledge using conditional entropy in rough set theory. First, the changing tendency of the conditional entropy of decision attributes giving condition attributes is studied from the viewpoint of information. Next, a new reduction algorithm based on conditional entropy is developed. Furthermore, our simulation results show that the algorithm can find the minimal reduction in most cases.
基金supported by RGC 203109,RGC 201508the FRGs of Hong Kong Baptist Universitythe PROCORE-France/Hong Kong Joint Research Scheme sponsored by the Research Grant Council of Hong Kong and the Consulate General of France in Hong Kong F-HK05/08T.
文摘In this paper,we introduce a novel hybrid variational model which generalizes the classical total variation method and the wavelet shrinkage method.An alternating minimization direction algorithm is then employed.We also prove that it converges strongly to the minimizer of the proposed hybrid model.Finally,some numerical examples illustrate clearly that the new model outperforms the standard total variation method and wavelet shrinkage method as it recovers better image details and avoids the Gibbs oscillations.
基金This work is supported by National Natural Science Foundation of China(No.60373111)National Foundation of China Scholarship Council+1 种基金Foundation for Research es on Science and Technology of Chongqing Education Committee(No.040505,No.040509)Foundation for Young or Middle Aged Excellent Key Tench-ers of Chongqing.
文摘Initiative-learning algorithms are characterized by and hence advantageousfor their independence of prior domain knowledge.Usually,their induced results could moreobjectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty,becauseuncertainty is an intrinsic common feature of and also an essential link between information systemsand their induced results.Obviously,the effectiveness of such initiative-learning framework isheavily dependent on the accuracy of system uncertainty measurements.Herein,a more reasonablemethod for measuring system uncertainty is developed based on rough set theory and the conception ofinformation entropy;then a new algorithm is developed on the bases of the new system uncertaintymeasurement and the Skowron's algorithm for mining prepositional default decision rules.Theproposed algorithm is typically initiative-learning.It is well adaptable to system uncertainty.Asshown by simulation experiments,its comprehensive performances are much better than those ofcongeneric algorithms.
