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New similarity functions

机译：新的相似函数

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摘要

In data science, there are some parameters that affect the accuracy of selected algorithms, regardless of their type. Type of data objects, membership assignments, and distance or similarity functions are the most important parameters that provide or not a proper environment for learning algorithms. The paper evaluates similarity functions as fundamental keys for membership assignments. The issues on conventional similarity functions are discussed in this paper. The paper introduces Weighted Feature Distance (WFD), and Prioritized Weighted Feature Distance (PWFD) to cover diversity in feature spaces. Most of the conventional distance functions compare data objects on vector space where any dominant feature may massively skew the final results. WFD functions perform better in supervised and unsupervised methods by comparing data objects on their feature spaces in addition to covering similarity on vector space. Prioritized Weighted Feature Distance (PWFD) works as same as WFD with ability to give priorities to desirable features. The accuracy of proposed functions are compared with other similarity functions on some data sets. Promising results show that the proposed functions work better than the other methods presented in this literature.
机译：在数据科学中，无论其类型如何，都有一些影响所选算法的准确性。数据对象类型，成员资格分配和距离或相似度函数是为学习算法提供的适当环境的最重要参数。本文评估了相似性函数作为会员分配的基本键。本文讨论了传统相似性功能的问题。本文介绍了加权特征距离（WFD），并优先考虑的加权特征距离（PWFD），以涵盖特征空间中的分集。大多数传统距离函数比较了矢量空间上的数据对象，其中任何主导特征可能会大量偏斜最终结果。 WFD函数通过比较其特征空间上的数据对象除了覆盖矢量空间上的相似性之外，在监督和无监督的方法中表现更好。优先考虑的加权特征距离（PWFD）与WFD相同，具有优先级的优先功能。将函数的准确性与某些数据集的其他相似性函数进行比较。有希望的结果表明，拟议的功能比在本文文献中提出的其他方法更好地工作。

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