摘要
The value difference metric （VDM） is one of the best-known and widely used distance functions for nominal attributes. This work applies the instance weighting technique to improve VDM. An instance weighted value difference met- ric （IWVDM） is proposed here. Different from prior work, IWVDM uses naive Bayes （NB） to find weights for train- ing instances. Because early work has shown that there is a close relationship between VDM and NB, some work on NB can be applied to VDM. The weight of a training instance x, that belongs to the class c, is assigned according to the dif- ference between the estimated conditional probability P（c／x） by NB and the true conditional probability P（c／x）, and the weight is adjusted iteratively. Compared with previous work, IWVDM has the advantage of reducing the time complex- ity of the process of finding weights, and simultaneously im- proving the performance of VDM. Experimental results on 36 UCI datasets validate the effectiveness of IWVDM.
The value difference metric （VDM） is one of the best-known and widely used distance functions for nominal attributes. This work applies the instance weighting technique to improve VDM. An instance weighted value difference met- ric （IWVDM） is proposed here. Different from prior work, IWVDM uses naive Bayes （NB） to find weights for train- ing instances. Because early work has shown that there is a close relationship between VDM and NB, some work on NB can be applied to VDM. The weight of a training instance x, that belongs to the class c, is assigned according to the dif- ference between the estimated conditional probability P（c／x） by NB and the true conditional probability P（c／x）, and the weight is adjusted iteratively. Compared with previous work, IWVDM has the advantage of reducing the time complex- ity of the process of finding weights, and simultaneously im- proving the performance of VDM. Experimental results on 36 UCI datasets validate the effectiveness of IWVDM.
