Authors:
Piotr Artiemjew
1
and
Sławomir K. Tadeja
2
Affiliations:
1
Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Poland
;
2
Institute of Applied Computer Science, Jagiellonian University in Kraków, Poland
Keyword(s):
Classification, Parallel Coordinates, Convnet, Pattern Recognition.
Abstract:
In this work, we assess the classification capability of visualized multidimensional data used in the decision- making process. We want to investigate if classification carried out over a graphical representation of the tabular data allows for statistically greater efficiency than the dummy classifier method. To achieve this, we have used a convolutional neural network (ConvNet) as the base classifier. As an input into this model, we used data presented in the form of 2D curves resulting from the Parallel Coordinates Plot (PCP) visualization. Our initial results show that the topological arrangement of attributes, i.e., the shape formed by the PCP curves of individual data items, can serve as an effective classifier. Tests performed on three different real-world datasets from the UCI Machine Learning Repository confirmed that classification efficiency is significantly higher than in the case of dummy classification. The new method provides an interesting approach to the classificatio
n of tabular data and offers a unique perspective on classification possibilities. In addition, we examined relevant information content potentially helpful in building hybrid classification models, e.g., in the classifier committee model. Moreover, our method can serve as an enhancement of the PCP visualization itself. Here, we can use our classification technique as a form of double-checking for the pattern identification task performed over PCP by the users.
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