A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Gayathri, C.
- An Effective and Accurate Fusion Result from Multi Class Ensemble Classification
Authors
1 Department of Computer Science, Karpagam University, Coimbatore – 641021, Tamilnadu, IN
2 Sri Sarada College for Women, Salem – 638016, Tamilnadu, IN
Source
Indian Journal of Education and Information Management, Vol 5, No 1 (2016), Pagination: 1-7Abstract
Background/Objectives: Financial fraud detection is the most challenging task in an online transaction oriented applications which concern more to provide the secured environment for the users. Various researches has been conducted previously that focus on providing the most secured environment to the users by finding and preventing the malicious patterns.
Methods/Statistical analysis: Classification is one of the most proved techniques for detecting the most malicious patterns that resides in the financial database by using which the malicious patterns can be identified. In our previous research work Optimal Ensemble Architecture Selection using Firefly and the dempster shafer theory based Ensemble is done for finding the fraudulent behaviour in the accurate manner. The ensemble classifier fusion approach used in the previous methodology called dempster shafer theory retrieves the fusion result as classifier output with more confidence value. This approach is computationally inefficient and doesn't concentrate on interrelation between different classifier results due to its additive measure property.
Findings: This problem is resolved in this work by introducing the fuzzy integral measure based ensemble fusion using sugeno integral (FIM-EFSSI) and the fuzzy integral measure based ensemble fusion using Choquet integral (FIMEFSCI). These approaches can find the better and accurate Ensemble result by considering the relation between the different classifier results.
Improvements/Applications: The experimental tests conducted were proves that the proposed approach provides better result than the existing approach in terms of improved classification accuracy in the matlab simulation environment.
Keywords
Ensembling Fusion, Sugeno, Choquet Fuzzy Integral, Fuzzy Values.Full Text
References
- Fabio Augusto Faria, Jefersson Alex dos Santos, Anderson Rocha and Ricardo da S. Torres. Automatic Classifier Fusion for Produce Recognition. Graphics, Patterns and Images (SIBGRAPI), 2012 25th SIBGRAPI Conference on 22-25 Aug. 2012.
- Mayank Vatsa, Richa Singh, Afzel Noore, Arun Ross. On the dynamic selection of biometric fusion algorithms. IEEE Transactions on Information Forensics and Security. 2010; 5(3), 470-479.
- Andy Jinhua Ma, Pong C. Yuen, Jian-Huang Lai. Linear dependency modeling for classifier fusion and feature combination. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013; 35(5), 1135-1148.
- Dominik Fisch, Edgar Kalkowski, Bernhard Sick. Knowledge fusion for probabilistic generative classifiers with data mining applications. IEEE Transactions on Knowledge and Data Engineering. 2014; 26(3), 652-666.
- Ville Hautamäki, Tomi Kinnunen, Filip Sedlák, Kong Aik Lee, Bin Ma, Haizhou Li. Sparse classifier fusion for speaker verification. IEEE Transactions on Audio, Speech, And Language Processing. 2013; 21(8), 1622-1631.
- Xi-Zhao Wang, Ran Wang, Hui-Min Feng, Hua-Chao Wang. A new approach to classifier fusion based on upper integral. IEEE Transactions on Cybernetics. 2014; 44(5), 620-635.
- Xiaoshuang Ma, Huanfeng Shen, Jie Yang, Liangpei Zhang, Pingxiang Li. Polarimetric-Spatial Classification of SAR Images Based on the Fusion of Multiple Classifiers. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. 2014; 7(3), 961-971.
- Wei Li, Saurabh Prasad, James E. Fowler. Decision Fusion in Kernel-Induced Spaces for Hyperspectral Image Classification. IEEE Transactions on Geoscience And Remote Sensing. 2014; 52(6), 3399-3411.
- Saisakul Chernbumroong, Shuang Cang, Hongnian Yu. Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People. IEEE Journal of Biomedical And Health Informatics. 2015; 19(1), 282-289.
- Richa Singh, Mayank Vatsa, Afzel Noore, Sanjay K. Singh. Dempster-Shafer Theory based Classifier Fusion for Improved Fingerprint Verification Performance. Computer Vision, Graphics and Image Processing. The series Lecture Notes in Computer Science. 2006; 4338, 941-949.
- Rachid Benmokhtar, Benoit Huet. Classifier fusion: combination methods for semantic indexing in video content. Artificial Neural Networks – ICANN. The series Lecture Notes in Computer Science. 2006; 4132, 65-74.
- Eulanda M. dos Santos, Robert Sabourin. Classifier ensembles optimization guided by population oracle. Evolutionary Computation (CEC), 2011 IEEE Congress on 5-8 June 2011.
- A. Prakash, C. Chandrasekar. An optimized Multiple Semi-Hidden Markov Model. Indian Journal of Science and Technology. 2015; 8(2), 165-171.
- Zahra Asheghi Dizaji, Farhad Soleimanian Gharehchopogh. A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation. Indian Journal of Science and Technology. 2015; 8(2), 128-133.
- Reza Effatnejad, Fazlollah Rouhi. Unit commitment in power system t by combination of dynamic programming (DP), genetic algorithm (GA) and particle swarm optimization (PSO), Indian Journal of Science and Technology, 2015; 8(2), 134-141.
- An Effective and Accurate Fusion Result from Multi Class Ensemble Classification
Authors
Source
Indian Journal of Education and Information Management, Vol 5, No 1 (2016), Pagination:Abstract
Financial fraud detection is a most challenging task in an online transaction oriented applications which concern more to provide the secured environment for the users. Various researches has been conducted previously that focus on providing the most secured environment to the users by finding and preventing the malicious patterns. Classification is one of the most proved techniques for detecting the most malicious patterns that resides in the financial database by using which the malicious patterns can be identified. In our previous research work Optimal Ensemble Architecture Selection using Firefly and the dempster shafer theory based Ensembling is done for finding the fraudulent behaviour in the accurate manner. The ensemble classifier fusion approach used in the previous methodology called dempster shafer theory retrieves the fusion result as classifier output with more confidence value. This approach is computationally inefficient and doesn’t concentrate on interrelation between different classifier results due to its additive measure property. This problem is resolved in this work by introducing the fuzzy integral measure based ensemble fusion using sugeno integral (FIM-EFSSI) and the fuzzy integral measure based ensemble fusion using Choquet integral (FIM-EFSCI). These approaches can find the better and accurate Ensembling result by considering the relation between the different classifier results. The experimental tests conducted were proves that the proposed approach provides better result than the existing approach in terms of improved classification accuracy in the matlab simulation environment.
Keywords
Ensembling Fusion, Sugeno, Choquet Fuzzy Integral, Fuzzy Values.Full Text