Titlebook: ;

阻止

Walter Tokarski,Reinhard Schmitz-ScherzerIn this Chapter we discuss methods for dealing with the problem of missing values already outlined in Chap.?.. There are four basic Strategies . and . which we examine on artificially damaged data sets.

從容

Transport- und Zuordnungsprobleme,In this chapter we discuss the problem of classification with use of descriptor indiscernibility ratio . and object indiscernibility ratio ..

恃強(qiáng)凌弱

D. Th. Henskes,K. Buser,U. KaulAssume that our data table does possess of . attributes in a set .,. objects, and . is the number of indiscernibility classes.

滔滔不絕的人

Wolfgang Donsbach,Mathias RentschIn Tables?., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., . and ., Figs.?., ., ., ., ., ., ., ., ., ., . and ., results are collected from which some parameters which may bear on the optimal granulation radius location are extracted.

Curmudgeon

Flagging

Barter

Largess

Tinea-Capitis

Naive Bayes Classifier on Granular Reflections: The Case of Concept-Dependent Granulation,In this chapter, we study the performance of Bayes classifier on granular structures. From results in Table ., we infer that Bayes classifier is well suited for granular cases as for most tested data its accuracy in best cases is better in granulated cases then in non-granulated ones.

Comprise

Granular Computing in the Problem of Missing Values,In this Chapter we discuss methods for dealing with the problem of missing values already outlined in Chap.?.. There are four basic Strategies . and . which we examine on artificially damaged data sets.