Object-to-group probabilistic distance measure for uncertain data classification

Behnam Tavakkol, Myong-Kee Jeong, Susan Albin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Uncertain objects, where each feature is represented by multiple observations or a given or fitted probability density function, arise in applications such as sensor networks, moving object databases and medical and biological databases. We propose a methodology to classify uncertain objects based on a new probabilistic distance measure between an uncertain object and a group of uncertain objects. This object-to-group probabilistic distance measure is unique in that it accounts separately for the correlations among the features within each class and within each object. We compare the proposed object-to-group classifier to two existing classifiers, namely, the K-Nearest Neighbor classifier on object means (certain-KNN) and the uncertain-naïve Bayes classifier. In addition, we compare the object-to-group classifier to an uncertain K-Nearest Neighbor classifier (uncertain-KNN), also proposed here, that uses existing probabilistic distance measures for object-to-object distances. We illustrate the advantages of the proposed classifiers with both simulated and real data.

Original languageEnglish (US)
Pages (from-to)143-151
Number of pages9
JournalNeurocomputing
Volume230
DOIs
StatePublished - Mar 22 2017

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Classification
  • Data mining
  • Probabilistic distance measures
  • Uncertain data

Fingerprint Dive into the research topics of 'Object-to-group probabilistic distance measure for uncertain data classification'. Together they form a unique fingerprint.

Cite this