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

Shangfei Wang, Xufa Wang, Hideyuki Takagi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

30 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2006 IEEE Congress on Evolutionary Computation, CEC 2006
ページ2195-2200
ページ数6
出版ステータス出版済み - 12 1 2006
イベント2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, カナダ
継続期間: 7 16 20067 21 2006

出版物シリーズ

名前2006 IEEE Congress on Evolutionary Computation, CEC 2006

その他

その他2006 IEEE Congress on Evolutionary Computation, CEC 2006
国/地域カナダ
CityVancouver, BC
Period7/16/067/21/06

All Science Journal Classification (ASJC) codes

  • 人工知能
  • ソフトウェア
  • 理論的コンピュータサイエンス

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