Projections as visual aids for classification system design

Author(s)

Rauber, Paulo E. ^{[1, 2]} ; Falcao, Alexandre X. ^[1] ; Telea, Alexandru C. ^[2]

Affiliation

^[1] Univ Estadual Campinas, Campinas, SP - Brazil

^[2] Univ Groningen, Dept Math & Comp Sci, Nijenborgh 9, NL-9747 AG Groningen - Netherlands

• Document type: Journal article
• Source: INFORMATION VISUALIZATION; v. 17, n. 4, p. 282-305, OCT 2018.
• Web of Science Citations: 5

Abstract

Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 12/24121-9 - Multi-label annotation of natural image databases with minimal supervision
• Grantee: Paulo Eduardo Rauber
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)