KI
2008
Springer
13 years 4 months ago
2008
Springer
We introduce a new stacking-like approach for multi-value classification. We apply this classification scheme using Naive Bayes, Rocchio and kNN classifiers on the well-known Reute...
KAIS
2008
13 years 4 months ago
2008
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, Page...
JMLR
2008
13 years 4 months ago
2008
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...
IJBIDM
2006
13 years 4 months ago
2006
: Appraisal of companies is an important business activity. We mainly apply Bayesian networks for this classification task for Japanese electric company data. Firstly, few standard...
CCR
2006
13 years 4 months ago
2006
The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular m...
DATAMINE
2010
13 years 4 months ago
2010
In this paper, we investigate how to modify the Naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive att...
AAAI
1996
13 years 5 months ago
1996
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competit...
UAI
2003
13 years 5 months ago
2003
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by thes...
TREC
2004
13 years 6 months ago
2004
We approached the problem of classifying papers for the TREC 2004 Genomics Track triage task as a four step process: feature generation, feature selection, classifier training, an...
IJCAI
2003
13 years 6 months ago
2003
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...