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LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning
International Journal of Information Technology & Decision Making ( IF 4.9 ) Pub Date : 2019-12-10 , DOI: 10.1142/s0219622019500457
Luis Morales 1 , José Aguilar 2, 3 , Danilo Chávez 1 , Claudia Isaza 4
Affiliation  

This paper proposes a new approach to improve the performance of Learning Algorithm for Multivariable Data Analysis (LAMDA). This algorithm can be used for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class, through the contributions of all its descriptors. LAMDA has the capability of creating new classes after the training stage. If an individual does not have enough similarity to the preexisting classes, it is evaluated with respect to a threshold called the Non-Informative Class (NIC), this being the novelty of the algorithm. However, LAMDA has problems making good classifications, either because the NIC is constant for all classes, or because the GAD calculation is unreliable. In this work, its efficiency is improved by two strategies, the first one, by the calculation of adaptable NICs for each class, which prevents that correctly classified individuals create new classes; and the second one, by computing the Higher Adequacy Degree (HAD), which grants more robustness to the algorithm. LAMDA-HAD is validated by applying it in different benchmarks and comparing it with LAMDA and other classifiers, through a statistical analysis to determinate the cases in which our algorithm presents a better performance.

中文翻译:

LAMDA-HAD,监督学习背景下 LAMDA 分类器的扩展

本文提出了一种提高多变量数据分析学习算法(LAMDA)性能的新方法。该算法可用于有监督和无监督学习,基于通过其所有描述符的贡献计算一个人对一个类的全局充足度 (GAD)。LAMDA 具有在培训阶段之后创建新课程的能力。如果一个人与先前存在的类没有足够的相似性,则会根据称为非信息类 (NIC) 的阈值对其进行评估,这就是该算法的新颖性。但是,LAMDA 在进行良好分类时存在问题,要么是因为 NIC 对于所有类都是恒定的,要么是因为 GAD 计算不可靠。在这项工作中,它的效率通过两种策略来提高,第一种,通过计算每个类别的适应性 NIC,这可以防止正确分类的个人创建新类别;第二个,通过计算更高的充分性(HAD),它赋予算法更多的鲁棒性。LAMDA-HAD 通过在不同的基准测试中应用并与 LAMDA 和其他分类器进行比较来验证,通过统计分析来确定我们的算法表现出更好性能的情况。
更新日期:2019-12-10
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