Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions w...Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.展开更多
In the applications of primary spectrum pyrometry,based on the dynamic range and the minimum sensibility of the sensor,the application issues,such as the measurement range and the measurement partition,were investigat...In the applications of primary spectrum pyrometry,based on the dynamic range and the minimum sensibility of the sensor,the application issues,such as the measurement range and the measurement partition,were investigated through theoretical analyses. For a developed primary spectrum pyrometer,the theoretical predictions of measurement range and the distributions of measurement partition were presented through numerical simulations. And the measurement experiments of high-temperature blackbody and standard temperature lamp were processed to further verify the above theoretical analyses and numerical results. Therefore the research in the paper provides the helpful supports for the applications of primary spectrum pyrometer and other radiation pyrometers.展开更多
基金funded by Hanoi University of Industry under Grant Number 27-2022-RD/HD-DHCN (URL:https://www.haui.edu.vn/).
文摘Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 50606033 and 50674079)
文摘In the applications of primary spectrum pyrometry,based on the dynamic range and the minimum sensibility of the sensor,the application issues,such as the measurement range and the measurement partition,were investigated through theoretical analyses. For a developed primary spectrum pyrometer,the theoretical predictions of measurement range and the distributions of measurement partition were presented through numerical simulations. And the measurement experiments of high-temperature blackbody and standard temperature lamp were processed to further verify the above theoretical analyses and numerical results. Therefore the research in the paper provides the helpful supports for the applications of primary spectrum pyrometer and other radiation pyrometers.
