Quantifying features using false nearest neighbors: An unsupervised approach

Jose Augusto Andrade Filho, Andre C P L F Carvalho, Rodrigo F. Mello, Salem Alelyani, Huan Liu

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

2 Scopus citations

Abstract

Real-world datasets commonly present high dimensional data, which means an increased amount of information. However, this does not always imply an improvement in learning technique performance. Furthermore, some features may be correlated or add unexpected noise, thereby reducing data clustering performance. This has motivated the development of feature selection methods to find the most relevant subset of features to describe data. In this work, we focus on the problem of unsupervised feature selection. The main goal is to define a method to identify the number of features to select after sorting them based on some criterion. This task is done by means of the False Nearest Neighbor technique, which is rooted in chaos theory. Results have shown that this technique gives a good approximate number of features to select. When compared to other techniques, in most of the analyzed cases, it maintains the quality of the generated partitions while selecting fewer features.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Pages994-997
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 - Boca Raton, FL, United States
Duration: Nov 7 2011Nov 9 2011

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

Other23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Country/TerritoryUnited States
CityBoca Raton, FL
Period11/7/1111/9/11

Keywords

  • Chaos Theory
  • Clustering
  • Machine Learning
  • Unsupervised Feature Selection

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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