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首页> 外文期刊>Intelligent data analysis >Adaptive geometric median prototype selection method for k-nearest neighbors classification
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Adaptive geometric median prototype selection method for k-nearest neighbors classification

机译：基于k最近邻居分类的自适应几何中值原型选择方法

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摘要

The k-nearest neighbors (kNN) algorithm is one of the most popular and simplest lazy learners. However, as the training dataset becomes larger, the algorithm suffers from the following drawbacks: large storage requirements, slow classification speed, and high sensitivity to noise. To overcome these drawbacks, we reduce the size of the training data by only selecting the necessary prototypes before the classification. This study proposes an extended prototype selection technique based on the geometric median (GM). We compare the proposed method with seven state-of-the-art prototype selection methods and 1NN as the baseline model. We use 25 datasets from the KEEL and UCI dataset repository website. The proposed method runs at least 3.5 times faster than the baseline model at the cost of slightly reduced accuracy. In addition, the classification accuracy and kappa value of the proposed method are comparable to those of all the state-of-the-art prototype selection methods considered.
机译：K-CORMALY邻居（KNN）算法是最受欢迎和最简单的懒惰学习者之一。但是，随着训练数据集变大，该算法遭受以下缺点：大量存储要求，分类速度慢，噪声敏感度高。为了克服这些缺点，我们通过仅在分类​​之前选择必要的原型来减少培训数据的大小。本研究提出了一种基于几何中位数（GM）的扩展原型选择技术。我们将建议的方法与七种最先进的原型选择方法和1NN进行比较，作为基线模型。我们使用keel和uci dataset存储库网站的25个数据集。该方法以略微降低的精度速度比基线模型快3.5倍。此外，所提出的方法的分类精度和κ值与考虑的所有最先进的原型选择方法的分类精度和kappa值相当。

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