2015 QuickSensitivityAnalysisforIncr

• (Okumura et al., 2015) ⇒ Shota Okumura, Yoshiki Suzuki, and Ichiro Takeuchi. (2015). “Quick Sensitivity Analysis for Incremental Data Modification and Its Application to Leave-one-out CV in Linear Classification Problems.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783347

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Quick+Sensitivity+Analysis+for+Incremental+Data+Modification+and+Its+Application+to+Leave-one-out+CV+in+Linear+Classification+Problems
• http://dl.acm.org/citation.cfm?id=2783258.2783347&preflayout=flat#citedby

Quotes

Author Keywords

• Classifier design and evaluation; feature evaluation and selection; incremental learning; leave-one-out cross- validation; sensitivity analysis

Abstract

We introduce a novel sensitivity analysis framework for large scale classification problems that can be used when a small number of instances are incrementally added or removed. For quickly updating the classifier in such a situation, incremental learning algorithms have been intensively studied in the literature. Although they are much more efficient than solving the optimization problem from scratch, their computational complexity yet depends on the entire training set size. It means that, if the original training set is large, completely solving an incremental learning problem might be still rather expensive. To circumvent this computational issue, we propose a novel framework that allows us to make an inference about the updated classifier without actually re-optimizing it. Specifically, the proposed framework can quickly provide a lower and an upper bounds of a quantity on the unknown updated classifier. The main advantage of the proposed framework is that the computational cost of computing these bounds depends only on the number of updated instances. This property is quite advantageous in a typical sensitivity analysis task where only a small number of instances are updated. In this paper we demonstrate that the proposed framework is applicable to various practical sensitivity analysis tasks, and the bounds provided by the framework are often sufficiently tight for making desired inferences.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 QuickSensitivityAnalysisforIncr	Shota Okumura Yoshiki Suzuki Ichiro Takeuchi			Quick Sensitivity Analysis for Incremental Data Modification and Its Application to Leave-one-out CV in Linear Classification Problems				10.1145/2783258.2783347		2015