Exploring KANs: Theory and Applications for Binary Classification

The lack of transparency and clarity in AI decisionmaking poses challenges of ethics and trust. This article presents <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K A N$</tex>, a class of networks developed by Ziming Liu among other researchers recently based on the Kolmogorov-Arnold representation theorem, designed to improve the performance and interpretability of classic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A I$</tex> algorithms. This article explains <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K A N$</tex> in an understandable way, with examples and addresses the binary classification problem. The results show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K A N$</tex> are competitive against other classification algorithms with the advantage that they are highly interpretable. Furthermore, in the datasets studied the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K A N$</tex> surpassed the MLP in the Friedman ranking. Moreover, it was found that its performance is similar, statistically speaking, to that of state-of-the-art classification algorithms. Therefore, KAN are a clear alternative when high performance and interpretability are required without the need to resort to post hoc tests.