Graph Based Classification Methods Using Inaccurate External Classifier Information

TL;DR

This paper introduces graph-based classification methods that incorporate inaccurate external classifier information, extending existing algorithms and proposing new efficient approaches, with evaluations showing improved speed, robustness, and accuracy.

Contribution

The paper develops a generalized framework for collective classification using external classifier data, extending LGC and WvRN methods, and introduces a new least squares regularization approach.

Findings

LSR and WvRN-extension outperform other methods in speed, robustness, and accuracy.

The proposed methods effectively utilize inaccurate external classifier information.

Experimental results on benchmark datasets validate the approaches.

Abstract

In this paper we consider the problem of collectively classifying entities where relational information is available across the entities. In practice inaccurate class distribution for each entity is often available from another (external) classifier. For example this distribution could come from a classifier built using content features or a simple dictionary. Given the relational and inaccurate external classifier information, we consider two graph based settings in which the problem of collective classification can be solved. In the first setting the class distribution is used to fix labels to a subset of nodes and the labels for the remaining nodes are obtained like in a transductive setting. In the other setting the class distributions of all nodes are used to define the fitting function part of a graph regularized objective function. We define a generalized objective function that handles both the settings. Methods like harmonic Gaussian field and local-global consistency (LGC) reported in the literature can be seen as special cases. We extend the LGC and weighted vote relational neighbor classification (WvRN) methods to support usage of external classifier information. We also propose an efficient least squares regularization (LSR) based method and relate it to information regularization methods. All the methods are evaluated on several benchmark and real world datasets. Considering together speed, robustness and accuracy, experimental results indicate that the LSR and WvRN-extension methods perform better than other methods.