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Intelligent feature extraction for ensemble of classifiers

P.V.W. Radtke, R. Sabourin, Tong Wong
2005 Eighth International Conference on Document Analysis and Recognition (ICDAR'05)  
This paper presents a two-level approach to create ensemble of classifiers based on intelligent feature extraction and multi-objective genetic optimization.  ...  The experimental results encourage further researches in this direction, as the optimized ensemble of classifiers outperforms the single classifier approach.  ...  Intelligent Feature Extractor In order to create a classifier, isolated handwritten symbols are modeled as representations, based on features extracted from specific foci of attention on images using zoning  ... 
doi:10.1109/icdar.2005.146 dblp:conf/icdar/RadtkeSW05 fatcat:ytaz64267zbihd27triwga2icq

A Multi-sensor Data Fusion Method for Intelligent Aging Condition Identification of Viscoelastic Sandwich Structure

Jinxiu Qu, Changquan Shi
2021 IEEE Access  
Second, an ensemble of SVM classifiers is trained on each of the sensitive feature sets extracted from different sensor data sets. These ensembles are then combined for data fusion using EISVM.  ...  In this paper, to extract effective feature information, MPE is introduced for feature extraction in intelligent aging condition identification of viscoelastic sandwich structure.  ... 
doi:10.1109/access.2021.3074655 fatcat:zvhauzmxsffwlg5tlrawjtrzfa

Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier  ...  Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%  ...  , EVS of 0.86 and MSLE of 0.16 as compared to other ensembling Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada Boost Classifier is found to  ... 
doi:10.35940/ijitee.l3473.1081219 fatcat:br3uzkermzbs3gam4gvbry6po4

Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

2020 International Journal of Engineering and Advanced Technology  
A stacked ensemble of five classifiers is proposed for accurate fault diagnosis and results are compared with conventional ensemble classifiers to prove its effectiveness  ...  Six "Statistical features" are extracted from the best Intrinsic mode function obtained through EMD and "Ensemble machine learning classifiers" are used for bearing fault diagnosis.  ...  Overview of Ensemble Classifiers As enormous amounts of vibration data are collected for bearing health monitoring, it becomes important to integrate different concepts for intelligent decision making  ... 
doi:10.35940/ijeat.c4836.029320 fatcat:hzrte7kly5eihlmhs5m6e6ky3i

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Preetha Jagannathan, Sujatha Rajkumar, Jaroslav Frnda, Parameshachari Bidare Divakarachari, Prabu Subramani, Laurie Cuthbert
2021 Wireless Communications and Mobile Computing  
Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification.  ...  To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification.  ...  Acknowledgments This publication was created thanks to the support from the Operational Program Integrated Infrastructure for the Project: Identification and possibilities of implementation of new technological  ... 
doi:10.1155/2021/5590894 fatcat:i6ot2uodzfanlklhiqjhqjikgm

A hybrid multiple classifier system for recognizing usual and unusual drilling events

Bilal Esmael, Arghad Arnaout, Rudolf K. Fruhwirth, Gerhard Thonhauser
2012 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings  
data and extracting statistical features.  ...  Experimental evaluation with real data and reports shows that the ensemble outperforms the base classifiers in every experiment, and the average classification accuracy is about 90% for usual events, and  ...  ACKNOWLEDGMENT We thank TDE Thonhauser Data Engineering GmbH for the support of this work and the permission to publish this paper.  ... 
doi:10.1109/i2mtc.2012.6229541 fatcat:rku3d2so25az7a4zsl56x356ye

Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines under Imbalanced Data

Feng Jia, Shihao Li, Hao Zuo, Jianjun Shen
2020 IEEE Access  
To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced  ...  However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect  ...  Since deep learning allows deep neural networks to accomplish the tasks of feature extraction and fault classification, the research of the intelligent fault diagnosis of machines using deep learning receives  ... 
doi:10.1109/access.2020.3006895 fatcat:zrhpyilntfcgjnbl6daqsydgiy

Domestic Cat Sound Classification Using Transfer Learning

Yagya Raj Pandeya, Joonwhoan Lee
2018 International Journal of Fuzzy Logic and Intelligent Systems  
Extracted feature are input to six various classifiers and ensemble techniques applied with predicted probabilities of all classifier results.  ...  The dataset was even not enough to select data driven approach for end to end learning, so we choose transfer learning for feature extraction.  ...  Acknowledgements The research leading to these result, authors would like to thank Korean Ministry of Education for funding.  ... 
doi:10.5391/ijfis.2018.18.2.154 fatcat:qszioyaio5dk7daeiw6tmnktoq

Preventing Poisoning Attacks On AI Based Threat Intelligence Systems

Nitika Khurana, Sudip Mittal, Aritran Piplai, Anupam Joshi
2019 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)  
Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information.  ...  The credibility of this information extracted from open sources, however, can often be questionable.  ...  other identified features for our meta-model, SVM classifier.  ... 
doi:10.1109/mlsp.2019.8918803 dblp:conf/mlsp/KhuranaMPJ19 fatcat:uvxukkktnvepfppfahoyujcsuq

Spammer Classification Using Ensemble Methods over Structural Social Network Features

Sajid Yousuf Bhat, Muhammad Abulaish, Abdulrahman A. Mirza
2014 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)  
In this paper, we evaluate the performance of some ensemble learning methods using community-based structural features extracted from an interaction network for the task of spammer detection in online  ...  Most commonly, individual classifiers are learnt over content-based features extracted from users' interactions and profiles to label them as spam/spammers or legitimate.  ...  ACKNOWLEDGEMENT The authors acknowledge the support provided by the King Abdulaziz City for Science and Technology (KACST), Kingdom of Saudi Arabia under the NPST project number 11-INF1594-02.  ... 
doi:10.1109/wi-iat.2014.133 dblp:conf/webi/BhatAM14 fatcat:dml4gl3jt5hg5nkmzrzcguzg4e

Preventing Poisoning Attacks on AI based Threat Intelligence Systems [article]

Nitika Khurana, Sudip Mittal, Anupam Joshi
2018 arXiv   pre-print
Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information.  ...  The credibility of this information extracted from open sources, however, can often be questionable.  ...  other identified features for our meta-model, SVM classifier.  ... 
arXiv:1807.07418v1 fatcat:a5tumy4cpvcklimjavtjg7h6ne

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Amal fouad Fouad, Beni Suef University, Hossam Moftah, Hesham Hefny, Beni Suef University, Cairo University
2020 International Journal of Intelligent Engineering and Systems  
The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.  ...  The whale optimization algorithm (WOA) plays a great role to reduce the numbers of HOG and Harr features from 38,640 to 120 features only which are less than .01% from all features.  ...  Feature extraction phase dimensionalities We have used HDWT and HOG features for features extraction process on the three types of tumors brain images.  ... 
doi:10.22266/ijies2020.0430.05 fatcat:eqak52mwv5cqbgn5dwsp7qtizi

Intelligent file scoring system for malware detection from the gray list

Yanfang Ye, Tao Li, Qingshan Jiang, Zhixue Han, Li Wan
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
In this paper, we develop an intelligent file scoring system (IFSS for short) for malware detection from the gray list by an ensemble of heterogeneous base-level classifiers derived by different learning  ...  To the best of our knowledge, this is the first work of applying such ensemble methods for malware detection.  ...  In this paper, we develop an intelligent file scoring system (IFSS for short) for malware detection from the gray list by an ensemble of heterogeneous base-level classifiers derived by different learning  ... 
doi:10.1145/1557019.1557167 dblp:conf/kdd/YeLJHW09 fatcat:epm4key6gfbe7lzqgy6d7pe72y

Web-of-Service Software Reusability Prediction using Heterogenous Ensemble Classifier

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
This paper develops a novel evolutionary computing assisted ensemble classification system for WoS software reusability prediction.  ...  The performance results affirmed that the present work ensemble classifier has better performance with respect to base classifiers.  ...  The detailed of the RSA is given as follows:  Step-1: Feature Set Selection In this step, extracted CK suite metrics for each class of the software are collected.  Step-2: Feature set (Data) Discretization  ... 
doi:10.35940/ijitee.i3281.0789s319 fatcat:r332beknubcyra5zp5xtddrp7q

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

2017 Symmetry  
This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs.  ...  For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym9080138 fatcat:xofie524pvfnjljoxghxfuir5u
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