Purifying data by machine learning with certainty levels

Shlomi Dolev, Guy Leshem, Reuven Yagel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A fundamental paradigm used for autonomic computing, self-managing systems, and decision-making under uncertainty and faults is machine learning. Machine learning uses a data-set, or a set of data-items. A data-item is a vector of feature values and a classification. Occasionally these data sets include misleading data items that were either introduced by input device malfunctions, or were maliciously inserted to lead the machine learning to wrong conclusions. A reliable learning algorithm must be able to handle a corrupted data-set. Otherwise, an adversary (or simply a malfunctioning input device that corrupts a portion of the data-set) may lead to inaccurate classifications. Therefore, the challenge is to find effective methods to evaluate and increase the certainty level of the learning process as much as possible. This paper introduces the use of a certainty level measure to obtain better classification capability in the presence of corrupted data items. Assuming a known data distribution (e.g., a normal distribution) and/or a known upper bound on the given number of corrupted data items, our techniques define a certainty level for classifications. Another approach suggests enhancing the random forest techniques to cope with corrupted data items by augmenting the certainty level for the classification obtained in each leaf in the forest. This method is of independent interest, that of significantly improving the classification of the random forest machine learning technique in less severe settings.

Original languageEnglish
Title of host publicationProceedings of the 3rd International ACM Workshop on Reliability, Availability, and Security, WRAS 2010
DOIs
StatePublished - 1 Dec 2010
Event3rd International ACM Workshop on Reliability, Availability, and Security, WRAS 2010 - Zurich, Switzerland
Duration: 29 Jul 201029 Jul 2010

Publication series

NameProceedings of the 3rd International ACM Workshop on Reliability, Availability, and Security, WRAS 2010

Conference

Conference3rd International ACM Workshop on Reliability, Availability, and Security, WRAS 2010
Country/TerritorySwitzerland
CityZurich
Period29/07/1029/07/10

Keywords

  • Certainty level
  • Data corruption
  • Machine learning
  • Pac learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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