How to find big-oh in your data set (and how not to)

C. C. McGeoch, D. Precup, P. R. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The empirical curve bounding problem is defined as follows. Suppose data vectors X, Y are presented such that E(Y[i]) = f(X[i]) where f(x) is an unknown function. The problem is to analyze X, Y and obtain complexity bounds O(gu(x)) and Ω(gl(x)) on the function f(x). As no algorithm for empirical curve bounding can be guaranteed correct, we consider heuristics. Five heuristic algorithms are presented here, together with analytical results guaranteeing correctness for certain families of functions. Experimental evaluations of the correctness and tightness of bounds obtained by the rules for several constructed functions f(x) and real datasets are described. A hybrid method is shown to have very good performance on some kinds of functions, suggesting a general, iterative refinement procedure in which diagnostic features of the results of applying particular methods can be used to select additional methods.

Original languageEnglish (US)
Title of host publicationAdvances in Intelligent Data Analysis
Subtitle of host publicationReasoning about Data - 2nd International Symposium, IDA-1997, Proceedings
EditorsXiaohui Liu, Paul Cohen, Michael Berthold
PublisherSpringer-Verlag
Pages41-52
Number of pages12
ISBN (Print)9783540633464
DOIs
StatePublished - 1997
Event2nd International Symposium on Intelligent Data Analysis, IDA 1997 - London, United Kingdom
Duration: Aug 4 1997Aug 6 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1280
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Symposium on Intelligent Data Analysis, IDA 1997
CountryUnited Kingdom
CityLondon
Period8/4/978/6/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    McGeoch, C. C., Precup, D., & Cohen, P. R. (1997). How to find big-oh in your data set (and how not to). In X. Liu, P. Cohen, & M. Berthold (Eds.), Advances in Intelligent Data Analysis: Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings (pp. 41-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1280). Springer-Verlag. https://doi.org/10.1007/bfb0052828