Integral inequality for nearest-neighbor classification
Integral inequality for nearest-neighbor classification
Let and be independent random variables, where and are densities with cumulative distribution functions and , respectively. Integral inequality conjecture. The inequality
holds. This inequality is presented as a sufficient-and-necessary reformulation of the expected-error comparison underlying the proposed nearest-neighbor rule, and its validity is left unresolved.
Progress summary
No public discussion or published progress on this conjecture was found.
No public discussion or published progress was found.
Current status (as of August 2026): The conjecture appears open, with no recorded public activity establishing either the inequality or a counterexample.
Sources & referencesView supporting material
Primary source
Kevin Bleakley, “Extreme change-point detection”, arXiv:2403.19237 (2024).
Solutions 1
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The integral can be strictly negative for explicit absolutely continuous probability densities.
For , define
and take
Both are probability densities. Write , and denote the conjectured integral by . The substitution
gives
At the ordered centers , the respective component-mass vectors are
For distinct centers , all corresponding midpoints lie in the same gap between the four supports. Thus is constant on that entire block, and
The complete exact calculation is
and hence
The same construction works with smooth densities: replace every by the translate of any normalized bump supported in . Every midpoint remains in the same gap, so the exact value is unchanged.