一種基于動態(tài)識別鄰域的免疫網(wǎng)絡(luò)分類算法及其性能分析
doi: 10.11999/JEIT141077
基金項目:
國家自然科學(xué)基金(61170199)和湖南省教育廳重點項目(11A004)資助課題
A Dynamic Recognition Neighborhood Based Immune Network Classification Algorithm and Its Performance Analysis
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摘要: 針對傳統(tǒng)免疫網(wǎng)絡(luò)分類算法在記憶細胞確定上缺乏有效的指導(dǎo)，該文提出一種基于動態(tài)識別鄰域的免疫網(wǎng)絡(luò)分類算法。算法采用核函數(shù)表示機制來描述抗體-抗原之間的親和度；利用抗原對構(gòu)造動態(tài)識別鄰域來指導(dǎo)抗體群體的進化，并選擇鄰域中距離對偶抗原最近的抗體為記憶細胞。算法被應(yīng)用于多分類問題及高維分類問題來進行算法性能分析，同時，算法被應(yīng)用于多個標準數(shù)據(jù)集的分類來評估算法的整體性能。分類結(jié)果表明該算法對于標準測試數(shù)據(jù)集有良好的分類性能，這說明基于動態(tài)識別鄰域的訓(xùn)練方法能夠有效地指導(dǎo)記憶細胞的生成，顯著地改善分類器的性能。
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關(guān)鍵詞:
- 人工智能 /
- 人工免疫系統(tǒng) /
- 免疫網(wǎng)絡(luò) /
- 抗原對 /
- 動態(tài)識別鄰域
Abstract: For lack of effective methods used by the traditional immune network algorithms to guide the memory cell determination, a dynamic recognition neighborhood based immune network classification algorithm is proposed. The algorithm uses a kernel function representation scheme to describe the antibody-antigen affinity, and constructs dynamic recognition neighborhood with using pair wise antigens to guide the antibody population evolution, in which the antibody nearest to the pairing antigen is determined as the memory cell. The algorithm is applied to multi-class problem and high dimensional classification problem to analyze the classification performance. Furthermore, the algorithm is used for many standard datasets classification to evaluate the algorithm overall performance. The results show that the proposed algorithm can achieve better classification performance, which indicates that the dynamic recognition neighborhood based training method is able to guide the memory cell generation effectively and improve the algorithm performance significantly. -
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