Estimation of Misclassification Error Using Bayesian Classifiers
نویسندگان
چکیده
Bayesian classifiers provide relatively good performance compared with other more complex algorithms. Misclassification ratio is very low for trained samples, but in the case of outliers the misclassification error may increase significantly. The usage of ‘summation hack’ method in Bayesian classification algorithm can reduce the misclassifications rate for untrained samples. The goal of this paper is to analyze the applicability of summation hack in Bayesian classifiers in general.
منابع مشابه
On optimal Bayesian classification and risk estimation under multiple classes
A recently proposed optimal Bayesian classification paradigm addresses optimal error rate analysis for small-sample discrimination, including optimal classifiers, optimal error estimators, and error estimation analysis tools with respect to the probability of misclassification under binary classes. Here, we address multi-class problems and optimal expected risk with respect to a given risk func...
متن کاملEstimation of the Parameters of the Lomax Distribution using the EM Algorithm and Lindley Approximation
Estimation of statistical distribution parameter is one of the important subject of statistical inference. Due to the applications of Lomax distribution in business, economy, statistical science, queue theory, internet traffic modeling and so on, in this paper, the parameters of Lomax distribution under type II censored samples using maximum likelihood and Bayesian methods are estimated. Wherea...
متن کاملBayesian adjustment for exposure misclassification in case-control studies.
Poor measurement of explanatory variables occurs frequently in observational studies. Error-prone observations may lead to biased estimation and loss of power in detecting the impact of explanatory variables on the response. We consider misclassified binary exposure in the context of case-control studies, assuming the availability of validation data to inform the magnitude of the misclassificat...
متن کاملEstimating Steatosis Prevalence in Overweight and Obese Children: Comparison of Bayesian Small Area and Direct Methods
Background Often, there is no access to sufficient sample size to estimate the prevalence using the method of direct estimator in all areas. The aim of this study was to compare small area’s Bayesian method and direct method in estimating the prevalence of steatosis in obese and overweight children. Materials and Methods: In this cross-sectional study, was conducted on 150 overweight and obese ...
متن کاملOptimal classifiers with minimum expected error within a Bayesian framework - Part II: Properties and performance analysis
In part I of this two-part study, we introduced a new optimal Bayesian classification methodology that utilizes the same modeling framework proposed in Bayesian minimum-mean-square error (MMSE) error estimation. Optimal Bayesian classification thus completes a Bayesian theory of classification, where both the classifier error and our estimate of the error may be simultaneously optimized and stu...
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:
دوره شماره
صفحات -
تاریخ انتشار 2009