This paper mainly deals with various classification algorithms namely, Bayes. NaiveBayes, Bayes. BayesNet, Bayes. NaiveBayesUpdatable, J48, Randomforest, and Multi Layer Perceptron. It analyzes the hepatitis patients from the UC Irvine machine learning repository. The results of the classification model are accuracy and time. Finally, it concludes that the Naive Bayes performance is better than...

9.1 Naive Bayes 9.1.

We address the position of subjectivism within Bayesian statistics. We argue, first, that the subjectivist Bayes approach is the only feasible method for tackling many important practical problems. Second, we describe the essential role of the subjectivist approach in scientific analysis. Third, we consider possible modifications to the Bayesian approach from a subjectivist viewpoint. Finally, ...

9.1 Naive Bayes 9.1.

Feature selection has proved to be an effective way to reduce the model complexity while giving a relatively desirable accuracy, especially, when data is scarce or the acquisition of some feature is expensive. However, the single selected model may not always generalize well for unseen test data whereas other models may perform better. Bayesian Model Averaging (BMA) is a widely used approach to...

Classifiers that aim at doing reliable predictions should rely on carefully elicited prior knowledge. Often this is not available so they should be able to start learning from data in condition of prior ignorance. This paper shows empirically, on an agricultural data set, that established methods of classification do not always adhere to this principle. Common ways to represent prior ignorance ...

Résumé. Un des défis actuels dans le domaine de la classification supervisée de documents est de pouvoir produire un modèle fiable à partir d’un faible volume de données. Avec un volume conséquent de données, les classifieurs fournissent des résultats satisfaisants mais les performances sont dégradées lorsque celui-ci diminue. Nous proposons, dans cet article, de nouvelles méthodes de pondérati...