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A New Data Processing Approach Research to Auto-fluorescence Spectrogram for Colorectal Carcinoma

机译:结直肠癌自荧光谱图的新数据处理方法研究

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Data classification is an important data mining role in biomedicine. This paper proposes a method to analyze Colorectal Carcinoma Auto-Fluorescence Spectrogram data based on Counting kNN Algorithm after analyzing the characteristics of biomedicine data. Though Counting kNN Algorithm for classification is simple and effective, it doesn''t deal with biomedicine data well. After analyzing the algorithm performance, a novel Counting kNN algorithm by index tree is presented. The new method improves the efficiency by using a tree structure index with the same accuracy. Experiments show that this method outperforms the distance-based voting kNN for accuracy, and ckNN for efficiency.
机译:数据分类是生物医学中的重要数据挖掘角色。本文提出了一种在分析生物医学数据特征后基于计数KNN算法分析结肠直肠癌自动荧光谱图数据的方法。虽然计算knn算法的分类是简单而有效的,但它并不处理生物医生数据。在分析算法性能之后,提出了一种新颖的索引树计数knn算法。新方法通过使用具有相同精度的树结构索引来提高效率。实验表明,该方法优于基于距离的投票KNN的精度,以及CKNN的效率。

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