Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing Image

Nowadays, multiple classifier system is widely used for land cover classification by remote sensing imagery. The performance of combined classifier is closely related to the selection of member classifiers, so it is necessary to analyze the diversity and consistency of member classifiers. In our study, consistency measures are studied and experimented from three levels: general consistency measure, binary prior measure and consistency of errors, and the result shows that it is feasible to find the effective set of member classifiers by some consistency measures. In land cover classification by remotely sensed classifiers, we can select optimal member classifiers by integrating different consistency criterions.

[1]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[2]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[3]  Jon Atli Benediktsson,et al.  Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments , 2007, MCS.

[4]  Jon Atli Benediktsson,et al.  The effect of classifier agreement on the accuracy of the combined classifier in decision level fusion , 2001, IEEE Trans. Geosci. Remote. Sens..

[5]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[6]  Jorma Laurikkala,et al.  Improving Identification of Difficult Small Classes by Balancing Class Distribution , 2001, AIME.

[7]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[8]  Yang Zhe-hai.  Combined Classifiers and Its Application in Hyperspectral Classification , 2007 .

[9]  Fabio Roli,et al.  Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..

[10]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[11]  Ian Witten,et al.  Data Mining , 2000 .

[12]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Nitesh V. Chawla,et al.  C4.5 and Imbalanced Data sets: Investigating the eect of sampling method, probabilistic estimate, and decision tree structure , 2003 .

[14]  José Alfredo Ferreira Costa,et al.  An Empirical Analysis of Under-Sampling Techniques to Balance a Protein Structural Class Dataset , 2006, ICONIP.

[15]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[16]  Panayiotis E. Pintelas,et al.  Mixture of Expert Agents for Handling Imbalanced Data Sets , 2003 .

[17]  David S. Doermann,et al.  Selection of classifiers for the construction of multiple classifier systems , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[18]  Salvatore J. Stolfo,et al.  Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection , 1998, KDD.

[19]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

[20]  Louisa Lam,et al.  Classifier Combinations: Implementations and Theoretical Issues , 2000, Multiple Classifier Systems.

[21]  Jorma Laaksonen,et al.  Using diversity of errors for selecting members of a committee classifier , 2006, Pattern Recognit..

[22]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[23]  Alexander Siegmund,et al.  Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data , 2003 .

[24]  Konstantinos Sirlantzis,et al.  On the relation between dependence and diversity in multiple classifier systems , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[25]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[26]  Xiang Li,et al.  The multiple classifiers combination method for improving the accuracy of remotely sensed data classification , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

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

[28]  Martin Zwick,et al.  Variance and Uncertainty Measures of Population Diversity Dynamics , 1995 .

[29]  Yi Yang,et al.  Combination of heterogeneous multiple classifiers based on evidence theory , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[30]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[31]  Matt J. Aitkenhead,et al.  A co-evolving decision tree classification method , 2008, Expert Syst. Appl..

[32]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[33]  Seong-Whan Lee,et al.  Experimental results on the construction of multiple classifiers recognizing handwritten numerals , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[34]  J. Fleiss.  Statistical methods for rates and proportions , 1974 .

[35]  Ching Y. Suen,et al.  Multiple Classifier Combination Methodologies for Different Output Levels , 2000, Multiple Classifier Systems.

[36]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .