Name
Abstract | ||
|---|---|---|
Traditional machine learning methods only consider relationships between feature values within individual data instances while disregarding the dependencies that link features across instances. In this work, we develop a general approach to supervised learning by leveraging higher-order dependencies between features. We introduce a novel Bayesian framework for classification named Higher Order Naive Bayes (HONB). Unlike approaches that assume data instances are independent, HONB leverages co-occurrence relations between feature values across different instances. Additionally, we generalize our framework by developing a novel data-driven space transformation that allows any classifier operating in vector spaces to take advantage of these higher-order co-occurrence relations. Results obtained on several benchmark text corpora demonstrate that higher-order approaches achieve significant improvements in classification accuracy over the baseline (first-order) methods. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04180-8_42 | ECML/PKDD (1) |
Keywords | DocType | Volume |
individual data instance,higher-order approach,higher-order co-occurrence relation,classification accuracy,honb leverages co-occurrence relation,link feature,feature value,higher-order dependency,text classification,novel bayesian framework,data instance,higher order dependencies,naive bayes,higher order,first order,machine learning,supervised learning,support vector machine,vector space,statistical relational learning | Conference | 5781 |
ISSN | Citations | PageRank |
0302-9743 | 13 | 0.59 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murat C. Ganiz | 1 | 13 | 0.59 |
| Nikita I Lytkin | 2 | 27 | 1.56 |
| William M. Pottenger | 3 | 266 | 23.14 |