Comparing Bayesian Network Classifiers

نویسندگان

  • Jie Cheng
  • Russell Greiner
چکیده

In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifiers: Naïve-Bayes, tree augmented Naïve-Bayes (TANs), BN augmented NaïveBayes (BANs) and general BNs (GBNs), where the GBNs and BANs are learned using two variants of a conditional independence based BN-learning algorithm. Experimental results show the GBNs and BANs learned using the proposing learning algorithms are competitive with (or superior to) the best classifiers based on both Bayesian networks and other formalisms, and that the computational time for learning and using these classifiers is relatively small. These results argue that BN classifiers deserve more attention in machine learning and data mining communities.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparing Case-Based Bayesian Network and Recursive Bayesian Multi-Net Classifiers

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in more accurate classifiers. We have recently examined a new type of Bayesian classifiers called Case-Based Bayesian Network (CBBN) classifiers. The basic idea is to partition the training data into semantically sound clusters. A local BN classifier is then learned independe...

متن کامل

Feature selection for Bayesian network classifiers using the MDL-FS score

When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features for such classifiers. To this end, we propose a new definition of the concept of redundancy in no...

متن کامل

Bayesian Conditional Gaussian Network Classifiers with Applications to Mass Spectra Classification

Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal predictions and is known to be robust to overfitting. In this work we introd...

متن کامل

Conditional Log-Likelihood for Continuous Time Bayesian Network Classifiers

Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter estimation is developed. Classification ac...

متن کامل

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing li...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1999