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On the optimality of the simple Bayesian classifier under zeroone loss
 MACHINE LEARNING
, 1997
"... The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containin ..."
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Cited by 601 (25 self)
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The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier’s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zeroone loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadraticloss optimality of the Bayesian classifier is in fact a secondorder infinitesimal fraction of the region of zeroone optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article’s results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
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Cited by 587 (22 self)
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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.
Selection of relevant features and examples in machine learning
 ARTIFICIAL INTELLIGENCE
, 1997
"... In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been mad ..."
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Cited by 423 (1 self)
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In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.
METIORE: A Personalized Information Retrieval System
 8 International Conference on User Modeling.UM'2001
, 2001
"... The idea of personalizing the interactions of a system is not new. ..."
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Cited by 14 (6 self)
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The idea of personalizing the interactions of a system is not new.
METIOREW: An Objective Oriented Content Based and Collaborative Recommending System
, 2001
"... The size of Internet has been growing very fast and many documents appear every day in the Net. Users find many problems to obtain the information that they really need. In order to help users in this task of finding relevant information, recommending systems were proposed. They give advice usin ..."
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Cited by 4 (0 self)
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The size of Internet has been growing very fast and many documents appear every day in the Net. Users find many problems to obtain the information that they really need. In order to help users in this task of finding relevant information, recommending systems were proposed. They give advice using two methods: the contentbased method that extracts information from the already evaluated documents by the user in order to obtain new related documents; the collaborative method that recommends documents to the user based on the evaluation by users with similar information need. In this paper we will present our approach through the employment of a user model and analyze some existing Web recommending systems and identify some problems that we try to solve in our system METIOREW. Some of the problems in document recommendation are: a) how to begin with document recommendation to users at the beginning of interaction when there is little or no knowledge on the user, b) how to make document recommendation to the user with changing information needs (objectives) without employing the general preferences of all the users but employing explicit individualized user model that integrates the user's objectives, c) how to provide access to the user's past history in order to review interesting documents related to specific objectives. The algorithms that we propose for calculating the degree of relevance of documents based on our user model is also explained.