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Sequential Feature Selection for Classification [chapter]

Thomas Rückstieß, Christian Osendorfer, Patrick van der Smagt
2011 Lecture Notes in Computer Science  
Our method performs a sequential feature selection that learns which features are most informative at each timestep, choosing the next feature depending on the already selected features and the internal  ...  Experiments on a handwritten digits classification task show significant reduction in required data for correct classification, while a medical diabetes prediction task illustrates variable feature cost  ...  One key ingredient for good classification results is feature selection (also called feature subset selection): filtering out irrelevant, noisy, misleading or redundant features.  ... 
doi:10.1007/978-3-642-25832-9_14 fatcat:kltj54r4b5blhoofaezqidnqlq

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

Naseer Ahmed Khan, Samer Abdulateef Waheeb, Atif Riaz, Xuequn Shang
2020 Brain Sciences  
To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset.  ...  Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls.  ...  Acknowledgments: We are extremely thankful to China Scholarship Council (CSC) and National Natural Science Foundation of China for giving us both the administrative and financial support to complete this  ... 
doi:10.3390/brainsci10100754 pmid:33086634 pmcid:PMC7603385 fatcat:njl5p2cirzblpj5dyegultcjvy

Adaptive Sequential Feature Selection for Pattern Classification

2012 Proceedings of the 4th International Joint Conference on Computational Intelligence   unpublished
Here, we propose a sequential adaptive algorithm that for a given testing sample selects features maximizing the expected information about its class.  ...  In such situations, one should select features in an adaptive manner, i.e. use different feature subsets for every testing sample.  ...  Adaptive feature selection Adaptivity: For classification problems with small training sets, we suggest to select features adaptively.  ... 
doi:10.5220/0004146804740482 fatcat:nizu5evfsnbjvpnjltuzvu46rm

An improved feature selection approach for chronic heart disease detection

S. J. Sushma, Tsehay Admassu Assegie, D. C. Vinutha, S. Padmashree
2021 Bulletin of Electrical Engineering and Informatics  
In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal  ...  Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity.  ...  The sequential feature selection algorithm is preferred for large datasets.  ... 
doi:10.11591/eei.v10i6.3001 fatcat:553g6lljpbepxbjhcdetfof5dm

Mining Discriminative Patterns for Classifying Trajectories on Road Networks

Jae-Gil Lee, Jiawei Han, Xiaolei Li, Hong Cheng
2011 IEEE Transactions on Knowledge and Data Engineering  
Based on our analysis, we contend that (frequent) sequential patterns are good feature candidates since they preserve this order information.  ...  In this paper, we present a framework for frequent pattern-based classification for trajectories on road networks.  ...  Please see Appendix for theoretical analysis. Feature Selection The objective of the feature selection step is to select highly discriminative sequential patterns.  ... 
doi:10.1109/tkde.2010.153 fatcat:tcmlunrsxjeplod3nkv62gmeqa

Minimizing data consumption with sequential online feature selection

Thomas Rückstieß, Christian Osendorfer, Patrick van der Smagt
2012 International Journal of Machine Learning and Cybernetics  
Depending on previously selected features and the internal belief of the classifier, a next feature is chosen by a sequential online feature selection that learns which features are most informative at  ...  Experiments on toy datasets and a handwritten digits classification task show significant reduction in required data for correct classification, while a medical diabetes prediction task illustrates variable  ...  classification and do not allow selection of different features for individual data samples.  ... 
doi:10.1007/s13042-012-0092-x fatcat:33qd5ebomjhqvekmk6kfahy37u

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Tatjana Liogienė, Gintautas Tamulevičius
2016 Electrical, Control and Communication Engineering  
Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets.  ...  In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme.  ...  Sequential Feature Selection In this study, we employed the Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques for the selection of feature subsets during every  ... 
doi:10.1515/ecce-2016-0005 fatcat:r6oyy7cjwjh7rbxo2b6oiblj3e

Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films

Suchada Tantisatirapong, Wongsakorn Preedanan
2020 ECTI Transactions  
The optimal feature set selected by the sequential forward selection yields lesser number of features and tends to give a higher degree of accuracy than the feature set selected by sequential backward  ...  Two feature selection approaches, the sequential forward selection method and sequential backward elimination method, integrated with a support vector machine classifier are examined to obtain the optimal  ...  Aiyada Aroonsri from the National Center for Genetic Engineering and Biotechnology (BIOTEC) for providing insightful suggestions and dataset.  ... 
doi:10.37936/ecti-eec.2020181.208115 fatcat:bgnpqf3j4bfmfdnubmjk37vpgi

Bayesian network classifiers versus selective -NN classifier

Franz Pernkopf
2005 Pattern Recognition  
This subset is established by means of sequential feature selection methods.  ...  Bayesian network classifiers outperform selective k-NN methods in terms of memory requirements and computational demands. This paper demonstrates the strength of Bayesian networks for classification.  ...  Bouchaffra for the valuable comments on this paper and to Ingo Reindl and Voest Alpine Donawitz Stahl for providing the data for the first experiment.  ... 
doi:10.1016/j.patcog.2004.05.012 fatcat:6ylgxmcervazld2q2wjl6gun3e

Efficient feature selection method using contribution ratio by random forest

Ryuei Murata, Yohei Mishina, Yuji Yamauchi, Takayoshi Yamashita, Hironobu Fujiyoshi
2015 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)  
To alleviate this problem, sequential backward selection (SBS) has come to be used as a method for selecting an effective number of features for classification.  ...  We performed an evaluation experiment to compare the proposed method with SBS and found that the former could significantly reduce feature selection time for the same dimension reduction rate.  ...  Typical wrapper methods for solving this optimization problem are round robin, sequential forward selection (SFS), sequential backward selection (SBS), and plus-s minus-r.  ... 
doi:10.1109/fcv.2015.7103746 dblp:conf/fcv/MurataMYYF15 fatcat:sn7q3s22kraobd7i5avkas4bgu

Implementation of practical computer aided diagnosis system for classification of masses in digital mammograms

Mohamed E. Elmanna, Yasser M. Kadah
2015 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE)  
Then we extracted a group of 59 texture and statistical features from the ROIs. Then we performed feature selection using Sequential Forward Selection and Sequential Floating Forward Selection.  ...  Finally we used K-Nearest Neighbor (KNN) classifier, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM) classifier for classification with leave-one-out  ...  In this work we used sequential forward selection (SFS) and Sequential floating forward selection (SFFS) for feature selection.  ... 
doi:10.1109/iccneee.2015.7381387 fatcat:iy47bw74wrhhrfmy5fexzjb3dy

Algorithms for feature selection: An evaluation

D. Zongker, A. Jain
1996 Proceedings of 13th International Conference on Pattern Recognition  
Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.  ...  A large number of algorithms have been proposed for doing feature subset selection.  ...  We have implemented the following well-known sequential algorithms: SFS Sequential forward selection SBS Sequential backward selection GSFS() Generalized sequential forward selection GSBS() Generalized  ... 
doi:10.1109/icpr.1996.546716 dblp:conf/icpr/ZongkerJ96 fatcat:i5ysljgznzcr3cz5ijuofnk4na

Adaptive Multi-level Backward Tracking for Sequential Feature Selection

Knitchepon Chotchantarakun, Ohm Sornil
2021 Journal of ICT Research and Applications  
Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets.  ...  OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.  ...  Conclusion Feature selection is very important for classification performance in the data mining process. This research focused on the improvement of early sequential feature selections.  ... 
doi:10.5614/itbj.ict.res.appl.2021.15.1.1 fatcat:5ht6nn7njnfp3pt457gp32ue4m

Feature selection for automatic classification of musical instrument sounds

Mingchun Liu, Chunru Wan
2001 Proceedings of the first ACM/IEEE-CS joint conference on Digital libraries - JCDL '01  
In this paper, we carry out a study on classification of musical instruments using a small set of features selected from a broad range of extracted ones by sequential forward feature selection method.  ...  Then, the sequential forward selection method is adopted to choose the best feature set to achieve high classification accuracy.  ...  The classification accuracies of the best feature set and the corresponding feature numbers are listed in the CONCLUSION In this paper, we use a sequential forward feature selection scheme to pick  ... 
doi:10.1145/379437.379663 dblp:conf/jcdl/LiuW01 fatcat:3gezvwqglngv3fe2zbr7boiu2m

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Yingying Guo, Jianfeng Qiu, Weizhao Lu
2020 Brain Sciences  
Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid  ...  could be used by machine learning algorithms for the classification of schizophrenia.  ...  Acknowledgments: We thank the Center for Biomedical Research Excellence (COBRE) for sharing their data. We also thank all participants in this study.  ... 
doi:10.3390/brainsci10080562 pmid:32824267 fatcat:kzeepsbtlbag7n3wbl4e6qpgei
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