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Supervised Classification of Diseases Based on an Improved Associative Algorithm
Mathematics ( IF 2.4 ) Pub Date : 2021-06-22 , DOI: 10.3390/math9131458
Raúl Jiménez-Cruz , José-Luis Velázquez-Rodríguez , Itzamá López-Yáñez , Yenny Villuendas-Rey , Cornelio Yáñez-Márquez

The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles.

中文翻译:

基于改进关联算法的疾病监督分类

线性关联器是经典的联想记忆模型。然而,由于其低性能,值得注意的是,很少有线性关联器应用程序已发布。这样做的原因是该模型要求表示模式的向量是正交的,这是一个很大的限制。一些研究人员试图创建向量的正交投影来馈送线性关联器。然而,该解决方案具有严重的缺点。本文提出了一个建议,当作为模式分类器时,它可以有效地提高线性关联器的性能。为此,该提议涉及使用强大的数学工具转换数据集:奇异值分解。为了进行实验,我们选择了两个类别的十四个医学数据集。所有数据集都表现出平衡,因此可以使用准确性作为性能度量。我们的提议的有效性与九个最重要方法(贝叶斯、最近邻、决策树、支持向量机和神经网络)的监督分类器进行了比较,包括三个分类器集成。Friedman 和 Holm 测试表明,我们的提议的性能明显优于九个分类器中的四个。此外,尽管其中三个是合奏,但与其他五个没有显着差异。Friedman 和 Holm 测试表明,我们的提议的性能明显优于九个分类器中的四个。此外,尽管其中三个是合奏,但与其他五个没有显着差异。Friedman 和 Holm 测试表明,我们的提议的性能明显优于九个分类器中的四个。此外,尽管其中三个是合奏,但与其他五个没有显着差异。
更新日期:2021-06-22
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