Competence region modelling in relational classification

Authors:
Tomasz Kajdanowicz;Tomasz Filipowski;Przemysław Kazienko;Piotr Bródka
Affiliations:
Wroclaw University of Technology, Wrocław, Poland;Wroclaw University of Technology, Wrocław, Poland, Research Engineering Center Sp. z o.o., Wrocław, Poland;Wroclaw University of Technology, Wrocław, Poland;Wroclaw University of Technology, Wrocław, Poland
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

Citing 15
Cited 0

Enhanced hypertext categorization using hyperlinks

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining the network value of customers

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Data Mining

Relational Data Mining
Adaptive Fraud Detection

Data Mining and Knowledge Discovery
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Communities of Interest

IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

ACM Transactions on Information Systems (TOIS)
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Exploratory Social Network Analysis with Pajek

Exploratory Social Network Analysis with Pajek
Why collective inference improves relational classification

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging relational autocorrelation with latent group models

MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Classification in Networked Data: A Toolkit and a Univariate Case Study

The Journal of Machine Learning Research
Probabilistic classification and clustering in relational data

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Evaluating Statistical Tests for Within-Network Classifiers of Relational Data

ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
User position measures in social networks

Proceedings of the 3rd Workshop on Social Network Mining and Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Relational classification is a promising branch of machine learning techniques for classification in networked environments which does not fulfil the iid assumption (independent and identically distributed). During the past few years, researchers have proposed many relational classification methods. However, almost none of them was able to work efficiently with large amounts of data or sparsely labelled networks. It is introduced in this paper a new approach to relational classification based on competence region modelling. The approach aims at solving large relational data classification problems, as well as seems to be a reasonable solution for classification of sparsely labelled networks by decomposing the initial problem to subproblems (competence regions) and solve them independently. According to preliminary results obtained from experiments performed on real world datasets competence region modelling approach to relational classification results with more accurate classification than standard approach.