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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Attribute-based Decision Graphs: A framework for multiclass data classification

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Author(s):
Bertini Junior, Joao Roberto ; Nicoletti, Maria do Carmo ; Zhao, Liang
Total Authors: 3
Document type: Journal article
Source: NEURAL NETWORKS; v. 85, p. 69-84, JAN 2017.
Web of Science Citations: 2
Abstract

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph - AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes. (C) 2016 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 12/00544-8 - Classifying stationary and non-stationary distributed data with the scarcity of labeled data through graph-based approaches
Grantee:João Roberto Bertini Junior
Support Opportunities: Scholarships in Brazil - Post-Doctoral