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References in periodicals archive ?
PFS's feature selection was evaluated using the C4.5 decision tree and Naive Bayes classifiers. The Reuters-21875 and WebKB corpora were used for the evaluation of the new method.
Comparing SVM and Naive Bayes classifiers for text categorization with Wikitology as knowledge enrichment.
Recent contributions have proposed using Bayesian classification rules with naive Bayes classifiers. The results of these studies demonstrated their frequent ability to do better than most existing techniques.
According to test results of TP (sensitivity) by using both 32 inputs for ANN and 8 inputs for Naive Bayes classifiers, olive oil types as 1,3,8,9,10,11, and 12 are correctly identified compared to the others.
Naive Bayes classifiers assume that all feature vectors are statistically independent and classify any test vector according to the Bayesian decision criterion given in (21) [63].
Naive Bayes Classifier. The naive Bayes classifiers (NBCs) [30] are a family of probabilistic classifiers depending on the Bayes' theorem with independence and normality assumptions among the variables.
Naive Bayes classifiers are linear classifiers that are known for being simple yet very efficient.
[9] used Naive Bayes classifiers to detect malware based on the presence of static features such as strings, byte sequences, and imported DLLs and APIs.
For some types of probability models, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.
In short, for running time efficiency, naive Bayes classifiers, K nearest neighbor and decision tree were basically fast, following by random forests, multi-class logic and linear support vector machines.
Similar to the equivalent .NET version, this month's release introduces new features that let developers filter spam email messages based on the naive Bayes classifiers. It also brings improvements to the size of generated PSTs.
In reference [6], by using Sliding Window and appropriate method in counting of word frequencies on spam and legitimate messages, and using variance of event frequencies for feature selection and by using SVM & Naive Bayes classifiers, the performance reached to 96.8 %.