Decision manifolds--a supervised learning algorithm based on self-organization

IEEE Trans Neural Netw. 2008 Sep;19(9):1518-30. doi: 10.1109/TNN.2008.2000449.


In this paper, we present a neural classifier algorithm that locally approximates the decision surface of labeled data by a patchwork of separating hyperplanes, which are arranged under certain topological constraints similar to those of self-organizing maps (SOMs). We take advantage of the fact that these boundaries can often be represented by linear ones connected by a low-dimensional nonlinear manifold, thus influencing the placement of the separators. The resulting classifier allows for a voting scheme that averages over the classification results of neighboring hyperplanes. Our algorithm is computationally efficient both in terms of training and classification. Further, we present a model selection method to estimate the topology of the classification boundary. We demonstrate the algorithm's usefulness on several artificial and real-world data sets and compare it to the state-of-the-art supervised learning algorithms.

MeSH terms

  • Algorithms*
  • Artificial Intelligence
  • Computer Simulation
  • Decision Support Techniques*
  • Models, Theoretical*
  • Neural Networks, Computer*
  • Pattern Recognition, Automated / methods*