Abstract
In this paper, a novel algorithm for enhancing the performance of classification is proposed. This new method provides rich information for clustering and outlier detection. We call it Natural Nearest Neighbor with Quality (3N-Q). Comparing to K-nearest neighbor and E-nearest neighbor, 3N-Q employs a completely different concept to find the nearest neighbors passively, which can adaptively and automatically get the K value. This value as well as distribution of neighbors and frequency of being neighbors of others offer precious foundation not only in classification but also in clustering and outlier detection. Subsequently, we propose a fitness function that reflects the quality of each training sample, retaining the good ones while eliminating the bad ones according to the quality threshold. From the experiment results we report in this paper, it is observed that 3N-Q is efficient and accurate for solving data mining problems.
Highlights
K-Nearest Neighbor (KNN) (Han & Kamber, 2006) is one of the most popular algorithms in knowledge discovery
We propose a fitness function that reflects the quality of each training sample, retaining the good ones while eliminating the bad ones according to the quality threshold
From the experiment results we report in this paper, it is observed that 3N-Q is efficient and accurate for solving data mining problems
Summary
K-Nearest Neighbor (KNN) (Han & Kamber, 2006) is one of the most popular algorithms in knowledge discovery. It is a supervised learning method where a new instance is classified based on majority of K-nearest neighbor category. The purpose of the algorithm is to classify the new object based on attributes and training samples. We conducted several experiments to evaluate the stability and accuracy of this proposed algorithm. Two other variables record the first to Kth neighbors of each training sample and the frequency of being other neighbors It provides the density and outlier information for us, which is the evidence of clustering and outlier detection.
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