### Abstract

A spatial metric, which can be used to systematically calculate the regularization parameter in an MRF formulation of a spatial classification problem was proposed. The standard way to measure classification accuracy is to calculate the percentage of correctly classified objects. Spatial accuracy achieved by classical regression, Spatial Autoregressive Regression (SAR), and Markov Random Field (MRF_hMETIS) was compared using Average Distance to Nearest Prediction (ANDP). It was observed that spatial regression takes two orders of magnitude more computation time relative to MRF_hMETIS approach using the public domain code.

Original language | English |
---|---|

Title of host publication | Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 |

Editors | J.H. Caulfield, S.H. Chen, H.D. Cheng, R. Duro, J.H. Caufield, S.H. Chen, H.D. Cheng, R. Duro, V. Honavar |

Pages | 245-250 |

Number of pages | 6 |

Publication status | Published - 1 Dec 2002 |

Event | Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 - Research Triange Park, NC, United States Duration: 8 Mar 2002 → 13 Mar 2002 |

### Publication series

Name | Proceedings of the Joint Conference on Information Sciences |
---|---|

Volume | 6 |

### Conference

Conference | Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 |
---|---|

Country | United States |

City | Research Triange Park, NC |

Period | 8/3/02 → 13/3/02 |

### ASJC Scopus subject areas

- Computer Science(all)

## Fingerprint Dive into the research topics of 'A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data'. Together they form a unique fingerprint.

## Cite this

*Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002*(pp. 245-250). (Proceedings of the Joint Conference on Information Sciences; Vol. 6).