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From neighbors to strengths - the k-strongest strengths (kSS) classification algorithm
Pattern Recognition Letters ( IF 3.255 ) Pub Date : 2020-06-23 , DOI: 10.1016/j.patrec.2020.06.020
Juan Aguilera; Luis C. González; Manuel Montes-y-Gómez; Roberto López; Hugo J. Escalante

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.
更新日期:2020-06-28

 

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