User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC
Wang, S.F., Wang, S., Takagi, H.: User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC. In: Proc. IEEE Congress on Evolutionary Computation, pp. 2195–2200. IEEE Press, New York (2006)
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 data-trained 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.
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