In this study we introduce the k-Strongest Strengths (kSS) Classification Algorithm, a novel approach for classification problems based on the well-known k-Nearest Neighbor (kNN) classifier. The proposed kSS method is motivated by an analogy to the Law of Universal Gravitation. The novelty of kSS resides in that instead of only using the neighbors’ labels to classify an unseen object it uses gravitation forces or strengths exerted by training objects. To incorporate this Newtonian concept into kSS, mass to training objects needs to be assigned. Following this idea we propose novel mass functions that exploit object’s topology properties within 11 data sets, which comprise binary and multi-class problems that present high and low imbalance ratio. Experiments show that kSS obtains an average f₁ score of 0.87, outperforming other popular Machine Learning methods such as Artificial Neural Networks (0.80), Decision Trees (0.75), other kNN variants (0.79), naïve Bayes (0.72) and Support Vector Machines (0.82). All these differences being statistically significant.

在这项研究中，我们介绍了k-Strongest优势（kSS）分类算法，这是一种基于著名的k-最近邻居（kNN）分类器的新型分类方法。所提出的kSS方法是由类似于万有引力定律的动机驱动的。kSS的新颖之处在于，它不仅利用邻居的标签对看不见的物体进行分类，而且还利用重力或训练物体施加的力量进行分类。为了将此牛顿概念纳入kSS，需要分配训练对象的质量。遵循这个想法，我们提出了新颖的质量函数，该函数利用11个数据集中的对象拓扑属性，这些二进制函数包含呈现高和低失衡比的二进制和多类问题。实验表明，kSS的平均值为f ₁得分为0.87，优于其他流行的机器学习方法，例如人工神经网络（0.80），决策树（0.75），其他kNN变体（0.79），朴素贝叶斯（0.72）和支持向量机（0.82）。所有这些差异具有统计意义。