An Efficient Iterative Algorithm to Explainable Feature Learning

Abstract

This paper summarizes a doctoral thesis introducing the new iterative approach to explainable feature learning. Features are learned in three steps during each iteration: feature construction, evaluation, and selection. We demonstrated superior performances compared to the state of the art on 13 of 15 test cases and the explainability of the learned feature representation for knowledge discovery.

References

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Authors

  • Dino Vlahek The Faculty of Electrical Engineering and Computer Science at the University of Maribor (UM FERI)

DOI:

https://doi.org/10.31449/inf.v48i2.6105

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Published

05/28/2024

How to Cite

Vlahek, D. (2024). An Efficient Iterative Algorithm to Explainable Feature Learning. Informatica, 48(2). https://doi.org/10.31449/inf.v48i2.6105

Issue

Vol. 48 No. 2 (2024)

Section

Thesis summary

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