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基于改進(jìn)蟻群算法的棉花異性纖維目標(biāo)特征選擇方法
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國家自然科學(xué)基金資助項目(30971693);教育部新世紀(jì)優(yōu)秀人才支持計劃資助項目(NCET—09—0731)


Feature Selection for Cotton Foreign Fiber Objects Based on Improved Ant Colony Algorithm
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    為提高基于機(jī)器視覺的棉花異性纖維在線分類的精度和速度，提出一種基于改進(jìn)蟻群算法的棉花異性纖維圖像目標(biāo)特征選擇方法。采用初始選擇概率預(yù)處理方案，設(shè)置特征初始概率，降低了冗余特征影響，縮短了算法搜索時間；利用分段變異運算及取優(yōu)舍劣策略，對棉花異性纖維的顏色、紋理、形狀3類特征進(jìn)行分段變異，避免了算法局部收斂，選出了全局最優(yōu)特征集。實驗結(jié)果表明，改進(jìn)的蟻群算法比基本蟻群算法優(yōu)化能力更強(qiáng)，搜索時間更短，優(yōu)化得到的棉花異性纖維特征子集的特征個數(shù)比原特征集減少了2/3，分類正確率由84%提高到93%。

    Abstract:

    An optimal feature subset selection method based on improved ant colony algorithm was presented. The initial probability of the feature was related to the ability of classification of the separate feature, which was advantageous to reduce the redundancy and the hunting zone of the optimized algorithm at the same time. Section variation of the feature set avoided local convergence. Experimental results indicated that the proposed algorithm further reduced the search time, got a smaller subset of the optimal feature set of cotton fibers and better classification performance. The classification accuracy rate increased from 84% to 93%.

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趙學(xué)華,李道亮,楊文柱,陳桂芬,于合龍,張馨.基于改進(jìn)蟻群算法的棉花異性纖維目標(biāo)特征選擇方法[J].農(nóng)業(yè)機(jī)械學(xué)報,2011,42(4):168-173. Zhao Xuehua, Li Daoliang, Yang Wenzhu, Chen Guifen, Yu Helong, Zhang Xin. Feature Selection for Cotton Foreign Fiber Objects Based on Improved Ant Colony Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(4):168-173.

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