Local Distribution in Neighborhood for Classification
This addresses a limitation in k-nearest-neighbor classification for domains requiring robust and efficient classification, though it appears incremental as it builds on existing neighborhood methods.
The paper tackles the problem of lost integral neighborhood information in k-nearest-neighbor classification by proposing a novel local learning method that organizes neighborhood information through local distribution, achieving improved classification performance with demonstrated scalability, efficiency, effectiveness, and robustness compared to state-of-the-art classifiers.
The k-nearest-neighbor method performs classification tasks for a query sample based on the information contained in its neighborhood. Previous studies into the k-nearest-neighbor algorithm usually achieved the decision value for a class by combining the support of each sample in the neighborhood. They have generally considered the nearest neighbors separately, and potentially integral neighborhood information important for classification was lost, e.g. the distribution information. This article proposes a novel local learning method that organizes the information in the neighborhood through local distribution. In the proposed method, additional distribution information in the neighborhood is estimated and then organized; the classification decision is made based on maximum posterior probability which is estimated from the local distribution in the neighborhood. Additionally, based on the local distribution, we generate a generalized local classification form that can be effectively applied to various datasets through tuning the parameters. We use both synthetic and real datasets to evaluate the classification performance of the proposed method; the experimental results demonstrate the dimensional scalability, efficiency, effectiveness and robustness of the proposed method compared to some other state-of-the-art classifiers. The results indicate that the proposed method is effective and promising in a broad range of domains.