On-demand numerosity reduction for object learning

Khamisi Kalegele, Johan Sveholm, Hideyuki Takahashi, Kazuto Sasai, Gen Kitagata, Tetsuo Kinoshita

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

1 Citation (Scopus)

Abstract

In Internet of Things, softwares shall enable their host objects (everyday-objects) to monitor other objects, take actions, and notify humans while using some form of reasoning. The ever changing nature of real life environment necessitates the need for these objects to be able to generalize various inputs inductively in order to play their roles more effectively. These objects shall learn from stored training examples using some generalization algorithm. In this paper, we investigate training sets requirements for object learning and propose a Stratified Ordered Selection (SOS) method as a means to scale down training sets. SOS uses a new instance ranking scheme called LO ranking. Everyday-objects use SOS to select training subsets based on their capacity (e.g. memory, CPU). LO ranking has been designed to broaden class representation, achieve significant reduction while offering same or near same analytical results and to facilitate faster on-demand subset selection and retrieval for resource constrained objects. We show how SOS outperforms other methods using well known machine learning datasets.

Original languageEnglish
Title of host publicationProceedings of the Workshop on Internet of Things and Service Platforms, IoTSP'11
DOIs
Publication statusPublished - 2011 Dec 1
EventWorkshop on Internet of Things and Service Platforms, IoTSP'11 - Tokyo, Japan
Duration: 2011 Dec 62011 Dec 9

Publication series

NameProceedings of the Workshop on Internet of Things and Service Platforms, IoTSP'11

Other

OtherWorkshop on Internet of Things and Service Platforms, IoTSP'11
CountryJapan
CityTokyo
Period11/12/611/12/9

Keywords

  • data reduction
  • learning
  • ranking
  • sampling

ASJC Scopus subject areas

  • Computer Networks and Communications

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