A Framework to Generate Synthetic Multi-label Datasets

Author(s)

Tomas, Jimena Torres ; Spolaor, Newton ; Cherman, Everton Alvares ; Monard, Maria Carolina

• Document type: Journal article
• Source: ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE; v. 302, p. 22-pg., 2014-02-25.

Abstract

A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. Although there are publicly available frameworks to generate synthetic single-label datasets, this is not the case for multi-label datasets, in which each instance is associated with a set of labels usually correlated. This work presents Mldatagen, a multi-label dataset generator framework we have implemented, which is publicly available to the community. Currently, two strategies have been implemented in Mldatagen: hypersphere and hypercube. For each label in the multi-label dataset, these strategies randomly generate a geometric shape (hypersphere or hypercube), which is populated with points (instances) randomly generated. Afterwards, each instance is labeled according to the shapes it belongs to, which defines its multi-label. Experiments with a multi-label classification algorithm in six synthetic datasets illustrate the use of Mldatagen. (AU)

• FAPESP's process: 11/12597-6 - Synthetic Data Generation for Multi-label Learning
• Grantee: Jimena Torres Tomas
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 10/15992-0 - Exploring label dependency in multilabel learning
• Grantee: Everton Alvares Cherman
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 11/02393-4 - Feature Selection for Multi-label Learning
• Grantee: Newton Spolaôr
• Support Opportunities: Scholarships in Brazil - Doctorate