OP-KNN: Method and Applications

This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed.

• Publication year

  2010

• Venue

  Advances in Artificial Neural Systems

• Publication date

  Unknown publication date

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1155/2010/597373
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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