Abstract

The K-Nearest Neighbor algorithm (KNN) is a method for classifying objects based on the k closest training objects. An object is classified by a majority vote of its nearest neighbors. “Closeness” is defined in terms of the similarity measure between two objects. KNN is not only simple, but also sometimes has high accuracy. However, the quality of KNN classification result depends on the similarity measure between two objects and the selection of k. Moreover, the average similarity of the majority nearest neighbors may be less than the one of the minority nearest neighbors. To deal with these problems, in this study, we propose a new classification approach called APCAS: classification based on the attribute values which are positively correlated with one of the class labels and the average similarity of the nearest neighbors in each class. First, we define a new similarity measure based on the attribute values which are positively correlated with one of the class labels. Second, we classify a new object using the average similarity of the nearest neighbors in each class without selecting k. Experimental results on the mushroom data show that APCAS achieves high accuracy.

Keywords:

KNN, nearest neighbor, positive correlation, similarity measure,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.