User fatigue reduction by an absolute rating data-trained predictor in IEC

Shangfei Wang, Xufa Wang, Hideyuki Takagi

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

30 Citations (Scopus)

Abstract

Predicting IEC users' evaluation characteristics is one way of reducing users' fatigue. However, users' relative evaluation appears as noise to the algorithm which learns and predicts the users' evaluation characteristics. This paper introduces the idea of absolute scale to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We first evaluate the effectiveness of the proposed method using seven benchmark functions instead of a human user. The experimental results show that the convergence speed of an IEC using the proposed absolute rating datatrained predictor is much faster than that of an IEC using a conventional predictor training with relative rating data. Next, the proposed algorithm is used in an individual emotion fashion image retrieval system. Experimental results of sign tests demonstrate that the proposed algorithm can alleviate user fatigue and has a good performance in individual emotional image retrieval.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages2195-2200
Number of pages6
Publication statusPublished - Dec 1 2006
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

Name2006 IEEE Congress on Evolutionary Computation, CEC 2006

Other

Other2006 IEEE Congress on Evolutionary Computation, CEC 2006
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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