Application of uncertainty measures on credal sets on the naive Bayesian classifier
نویسنده
چکیده
The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
منابع مشابه
Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification
Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions ...
متن کاملTree-Based Credal Networks for Classification
Bayesian networks are models for uncertain reasoning which are achieving a growing importance also for the data mining task of classification. Credal networks extend Bayesian nets to sets of distributions, or credal sets. This paper extends a state-of-the-art Bayesian net for classification, called tree-augmented naive Bayes classifier, to credal sets originated from probability intervals. This...
متن کاملUpper entropy of credal sets. Applications to credal classification
We present an application of the measure of entropy for credal sets: as a branching criterion for constructing classification trees based on imprecise probabilities which are determined with the imprecise Dirichlet model. We also justify the use of upper entropy as a global uncertainty measure for credal sets and present a deduction of this measure. We have carried out several experiments in wh...
متن کاملA New Hybrid Framework for Filter based Feature Selection using Information Gain and Symmetric Uncertainty (TECHNICAL NOTE)
Feature selection is a pre-processing technique used for eliminating the irrelevant and redundant features which results in enhancing the performance of the classifiers. When a dataset contains more irrelevant and redundant features, it fails to increase the accuracy and also reduces the performance of the classifiers. To avoid them, this paper presents a new hybrid feature selection method usi...
متن کاملLikelihood-Based Naive Credal Classifier
Bayesian Classifiers Learn joint distribution P(C,F) Assign to f the most probable class label argmaxc′∈C P(c′, f̃) This defines a classifier, i.e., a map: (F1× . . .×Fm)→ C Credal Classifiers Learn joint credal set P(C,F) Set of optimal classes (e.g., according to maximality ) {c′ ∈ C |@c′′ ∈ C ,∀P ∈ P : P(c′′|f̃) > P(c′|f̃)} This defines a credal classifier, i.e., (F1× . . .×Fm)→ 2 May return mo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Int. J. General Systems
دوره 35 شماره
صفحات -
تاریخ انتشار 2006