Multi Label Spatial Semi Supervised Classification using Spatial Associative Rule Mining and Evolutionary Algorithms

Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. In this paper we adapt problem transformation for the multi label classification. We use hybrid evolutionary algorithm for the optimization in the generation of spatial association rules, which addresses single label. MOGA is used to combine the single labels into multi labels with the conflicting objectives predictive accuracy and comprehensibility. Semi supervised learning is done through the process of rule cover clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is simulated and the results are compared with MOGA based associative classifier, which out performs the existing.

[1]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[2]  Alexander H. Waibel,et al.  Unsupervised training of a speech recognizer: recent experiments , 1999, EUROSPEECH.

[3]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  Naonori Ueda,et al.  Exploitation of Unlabeled Sequences in Hidden Markov Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

[8]  Hongjun Lu,et al.  CBC: clustering based text classification requiring minimal labeled data , 2003, Third IEEE International Conference on Data Mining.

[9]  Jiawei Han,et al.  A progressive refinement approach to spatial data mining , 1999 .

[10]  George Nagy,et al.  Self-corrective character recognition system , 1966, IEEE Trans. Inf. Theory.

[11]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[12]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[13]  Grigorios Tsoumakas,et al.  Multi-Label Classification , 2009, Database Technologies: Concepts, Methodologies, Tools, and Applications.

[14]  Xian-Jun Shi,et al.  A Genetic Algorithm-Based Approach for Classification Rule Discovery , 2008, 2008 International Conference on Information Management, Innovation Management and Industrial Engineering.

[15]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[16]  Fabio Roli.  Semi-supervised Multiple Classifier Systems: Background and Research Directions , 2005, Multiple Classifier Systems.

[17]  T. Kalamboukis,et al.  Text Classification Using Clustering , 2006 .

[18]  Sandip Sen,et al.  Using real-valued genetic algorithms to evolve rule sets for classification , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[19]  Satchidananda Dehuri,et al.  Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases , 2008, Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases.

[20]  Tzay Y. Young,et al.  On decision-directed estimation and stochastic approximation (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[21]  Martin Ester,et al.  A multi-relational approach to spatial classification , 2009, KDD.

[22]  Matthias Seeger,et al.  Learning from Labeled and Unlabeled Data , 2010, Encyclopedia of Machine Learning.

[23]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[24]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[25]  Jiawei Han,et al.  CPAR: Classification based on Predictive Association Rules , 2003, SDM.

[26]  Elearn Limited,et al.  Information and knowledge management , 2005 .

[27]  Lu Zhao,et al.  Association Rule Analysis of Spatial Data Mining Based on Matlab-A Case of Ancheng Township in China , 2008, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008).

[28]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[29]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[30]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[31]  Salem Chakhar,et al.  Towards a typology of spatial decision problems , 2004 .

[32]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[33]  Rajib Mall,et al.  Application of elitist multi-objective genetic algorithm for classification rule generation , 2008, Appl. Soft Comput..

[34]  George Nagy DocLab.  Classifiers That Improve with Use , 2004 .

[35]  Grigorios Tsoumakas,et al.  Clustering based multi-label classification for image annotation and retrieval , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[36]  M. Dorigo,et al.  1 Positive Feedback as a Search Strategy , 1991 .

[37]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[38]  J. Deneubourg,et al.  Trails and U-turns in the Selection of a Path by the Ant Lasius niger , 1992 .

[39]  R. Matthews,et al.  Ants. , 1898, Science.

[40]  Rajib Mall,et al.  Predictive and comprehensible rule discovery using a multi-objective genetic algorithm , 2006, Knowl. Based Syst..