Data Complexity in Machine Learning and Novel Classification Algorithms

Category: ML

Overall Rating

1.3/5 (9/35 pts)

While the concept of understanding an example's "complexity contribution" remains a valuable research direction for data curation, the specific methods proposed in the paper suffer from significant practical and theoretical limitations...
...and have largely been surpassed by more robust and scalable techniques in modern machine learning.
The paper serves as a historical record of exploring these ideas but does not offer a unique, actionable path for direct revival today.

The core idea of defining and utilizing data complexity based on the shortest program/hypothesis length (approximating Kolmogorov complexity/MDL) for tasks like data decomposition and pruning holds significant latent potential.
Explicitly quantifying and using data complexity in this manner, especially through concepts like "principal subsets" and "complexity contribution," feels less explored in contemporary mainstream ML research compared to its potential utility.
The practical challenges highlighted in the paper for applying these concepts (e.g., incomputability of ideal measures, computational infeasibility of finding principal subsets) are directly addressed by modern technological advancements.
Imagine training systems not just to learn from data, but also to continuously analyze the data's intrinsic complexity landscape.

The foundational concept of Data Complexity presented here... has largely failed to translate into practical, scalable tools for modern machine learning.
The paper likely faded from prominence due to a combination of theoretical intractability and practical shortcomings.
The proposed Perceptron RCD algorithm... demonstrably overfits on real-world data compared to regularized methods like averaged perceptrons or Soft-SVM.
The practical data complexity measures (SVM support vectors) are ad hoc proxies whose theoretical link to universal complexity is tenuous and whose effectiveness might not generalize beyond the specific model used.

Ignore