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首页> 外文会议>Document recognition and retrieval XVII >Ant Colony Optimization with Selective Evaluation for Feature Selection in Character Recognition
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Ant Colony Optimization with Selective Evaluation for Feature Selection in Character Recognition

机译：基于选择性评估的蚁群算法在字符识别中的特征选择

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摘要

This paper analyzes the size characteristics of character recognition domain with the aim of developing a feature selection algorithm adequate for the domain. Based on the results, we further analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. We propose a novel scheme called selective evaluation to improve convergence of ACO. The scheme cut down the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.
机译：本文分析了字符识别域的大小特征，以期开发出适合该域的特征选择算法。根据结果​​，我们进一步分析了三种流行的特征选择算法，贪婪算法，遗传算法和蚁群优化的时序要求。为了进行严格的时序分析，我们采用了原子操作的概念。我们提出了一种称为选择性评估的新方案，以提高ACO的收敛性。该方案通过排除不必要或不太有希望的候选解决方案的评估，减少了计算量。由于有价值的信息，信息素追踪可帮助识别这些解决方案，因此该方案可在ACO中实现。实验结果表明，具有选择性评估的ACO在时序要求和识别性能上都有希望。

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