A rough set approach to the discovery of classification rules in spatial data

This paper proposes a novel rough set approach to discover classification rules in real‐valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real‐valued or integer‐valued decision system into an interval‐valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real‐life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.

[1]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[2]  Jerzy W. Grzymala-Busse,et al.  Global discretization of continuous attributes as preprocessing for machine learning , 1996, Int. J. Approx. Reason..

[3]  J. Chan,et al.  Detecting the nature of change in an urban environment : A comparison of machine learning algorithms , 2001 .

[4]  Wei-Zhi Wu,et al.  Approaches to knowledge reduction based on variable precision rough set model , 2004, Inf. Sci..

[5]  Ari Visa,et al.  Generalisation of Neural Network Based Segmentation Results for Classification Purposes , 1997 .

[6]  Xie Ping,et al.  Auto-identification of forest fire-points in NOAA images based on neural network , 2001 .

[7]  Ola Ahlqvist,et al.  Rough classification and accuracy assessment , 2000, Int. J. Geogr. Inf. Sci..

[8]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[9]  Ramin Yasdi,et al.  Combining Rough Sets Learning- and Neural Learning-method to deal with uncertain and imprecise information , 1995, Neurocomputing.

[10]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[11]  Ola Ahlqvist,et al.  Please Scroll down for Article International Journal of Geographical Information Science Using Uncertain Conceptual Spaces to Translate between Land Cover Categories Using Uncertain Conceptual Spaces to Translate between Land Cover Categories , 2022 .

[12]  Marzena Kryszkiewicz.  Comparative study of alternative types of knowledge reduction in inconsistent systems , 2001, Int. J. Intell. Syst..

[13]  Jusheng Mi,et al.  Incomplete information system andits optimal selections , 2004 .

[14]  Thomas Bittner,et al.  Vagueness and Rough Location , 2002, GeoInformatica.

[15]  Wei-Zhi Wu,et al.  Knowledge reduction in random information systems via Dempster-Shafer theory of evidence , 2005, Inf. Sci..

[16]  Wang Xinzhou,et al.  Development of a data mining method for land control , 2001 .

[17]  Michael F. Worboys,et al.  Imprecision in Finite Resolution Spatial Data , 1998, GeoInformatica.

[18]  Paul M. Mather,et al.  The role of feature selection in artificial neural network applications , 2002 .

[19]  S. Tsumoto,et al.  Rough set methods and applications: new developments in knowledge discovery in information systems , 2000 .

[20]  Yee Leung,et al.  Maximal consistent block technique for rule acquisition in incomplete information systems , 2003, Inf. Sci..

[21]  John F. Roddick,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[22]  Fabio Roli,et al.  Neurocomputation in Remote Sensing Data Analysis , 1997, Springer Berlin Heidelberg.

[23]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[24]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[25]  Kwong-Sak Leung,et al.  An Intelligent Expert System Shell for Knowledge-Based Geographical Information Systems: 1. The Tools , 1993, Int. J. Geogr. Inf. Sci..

[26]  Raghbir Singh Sandhu,et al.  Intelligent spatial decision support systems , 1998 .

[27]  Li Deren,et al.  Geo-rough space , 2003 .

[28]  Jiawei Han,et al.  Data-Driven Discovery of Quantitative Rules in Relational Databases , 1993, IEEE Trans. Knowl. Data Eng..

[29]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .

[30]  Wei-Zhi Wu,et al.  Approaches to knowledge reductions in inconsistent systems , 2003, Int. J. Intell. Syst..

[31]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[32]  Tsau Young Lin,et al.  Rough Set Methods and Applications , 2000 .

[33]  Kwong-Sak Leung,et al.  An Intelligent Expert System Shell for Knowledge-Based Geographical Information Systems: 2. Some Applications , 1993, Int. J. Geogr. Inf. Sci..

[34]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[35]  Ola Ahlqvist,et al.  Rough and fuzzy geographical data integration , 2003, Int. J. Geogr. Inf. Sci..

[36]  Minhe Ji,et al.  Using fuzzy sets to improve cluster labelling in unsupervised classification , 2003 .

[37]  M. Worboys.  Computation with imprecise geospatial data , 1998 .

[38]  John G. Stell,et al.  Stratified Map Spaces: A Formal Basis for Multi-resolution Spatial Databases , 2001 .

[39]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[40]  Tapio Elomaa,et al.  Principles of Data Mining and Knowledge Discovery , 2002, Lecture Notes in Computer Science.

[41]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[42]  Yee Leung,et al.  Knowledge acquisition in incomplete information systems: A rough set approach , 2006, Eur. J. Oper. Res..

[43]  Andrzej Czyzewski,et al.  Rough Set Methods and Applications , 1998, Rough Sets and Current Trends in Computing.

[44]  Manfred M. Fischer,et al.  Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification , 1997 .

[45]  Jon Atli Benediktsson,et al.  Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[46]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[47]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[48]  Paul M. Mather,et al.  The use of backpropagating artificial neural networks in land cover classification , 2003 .

[49]  P. S. Roy,et al.  Landscape dynamics in the Maipo Ramsar Wetland site. , 2003 .

[50]  F. Wang.  Improving remote sensing image analysis through fuzzy information representation , 1990 .

[51]  Luis Samaniego,et al.  Fuzzy rule-based classification of remotely sensed imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[52]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .