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Correlation-based Feature Selection Strategy in Neural Classification

Krzysztof Michalak, Halina Kwasnicka
2006 Sixth International Conference on Intelligent Systems Design and Applications  
In this paper, a modified pairwise selection strategy is proposed.  ...  correlation between the features.  ...  Let m p and m c denote the mean classification error yielded by the pairwise selection strategy and the correlation-based strategy, respectively.  ... 
doi:10.1109/isda.2006.128 dblp:conf/isda/MichalakK06 fatcat:fxpmnzbsibbr5pddhg7vttyoe4

Correlation-based Feature Ordering for Classification based on Neural Incremental Attribute Learning

Ting Wang, Sheng-Uei Guan, Fei Liu
2012 International Journal of Machine Learning and Computing  
Experimental results confirm that correlation-based feature ordering can produce better classification results than contribution-based approaches, feature orderings with theoriginal sequence sorted in  ...  However, besides contribution, correlations among input features and output categories are also very important to the final classification result, which has not yet been researched in feature ordering  ...  To solve complex classification problems, some dimensional reduction strategies like feature selection have been employed [1, 2] .  ... 
doi:10.7763/ijmlc.2012.v2.242 fatcat:qxivv7rmvzg4vbcezr5zk2a5se

MII: A Novel Text Classification Model Combining Deep Active Learning with BERT

Anman Zhang, Bohan Li, Wenhuan Wang, Shuo Wan, Weitong Chen
2020 Computers Materials & Continua  
As for the process of active learning, we design an instance selection strategy based on posterior probabilities Margin, Intra-correlation and Inter-correlation (MII).  ...  The effect of learner is compared while the effect of sampling strategy and text classification is assessed from three real datasets.  ...  Instance correlation This scheme uses feature-based, label-based and graph-based correlation among instances to determine the most informative samples.  ... 
doi:10.32604/cmc.2020.09962 fatcat:wc7fn6cs4nh3pe3kxsafwelbpi

Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

P Kalaivani, St.Joseph's College of Engineering, Chennai
2020 Indian Journal of Science and Technology  
vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews.  ...  The backpropagation algorithm is used to improve classification accuracy in the neural network learning and IG and PCA are used in sentiment classification to reduce the feature length and training time  ...  The p art-of-speech (POS) based attribute selection and the feature relation based attribute selection was used in sentiment classification.  ... 
doi:10.17485/ijst/v13i28.927 fatcat:bjiibm5exncwlgljyocgtkb5cy

An Improved DeepNN with Feature Ranking for Covid-19 Detection

Noha E. El-Attar, Sahar F. Sabbeh, Heba Fasihuddin, Wael A. Awad
2022 Computers Materials & Continua  
In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients' electronic medical records  ...  Additionally, it outperforms the other models in classification results as well as time.  ...  Correlation-based feature selection, fast correlated-based filter, and Relief are examples of filter methods [4] .  ... 
doi:10.32604/cmc.2022.022673 fatcat:vqrlsla2kjealidyct4n36aflu

Artificial Neural Network for Multiclass Recognition and its Application to the Thyroid Functional State

Galyna Kriukova, Serhii Radchenko, Oleksandr Sudakov
2017 Naukovì Vìstì Nacìonalʹnogo Tehnìčnogo Unìversitetu Ukraïni Kiïvsʹkij Polìtehnìčnij Institut  
ANN-based models of the distribution of class labels in terms of predictor features are constructed, trained and validated for datasets of clinical records.  ...  The goal is to analyze and compare performance of ANN-based classifiers on various datasets for further improvement of model selection strategy. Methods.  ...  In this paper we demonstrate that application of Linear Functional Strategy (LFS) for diagnosis classification based on USI images can provide the same or even better efficiency compared to neural networks  ... 
doi:10.20535/1810-0546.2017.1.93128 fatcat:dllwtchbw5f3tg73fbgs7nspuu

Hybrid (Generalization-Correlation) Method for Feature Selection in High Dimensional DNA Microarray Prediction Problems [chapter]

Yasel Couce, Leonardo Franco, Daniel Urda, José L. Subirats, José M. Jerez
2011 Lecture Notes in Computer Science  
This work analyzes the characteristics of the features selected by two wrapper methods, the first one based on artificial neural networks (ANN) and the second in a novel constructive neural network (CNN  ...  The process typically involves a feature selection step, important in order to increase the accuracy and speed of the classifiers.  ...  for selection of informative genes in DNA microarray experiments through applying a hybrid model of constructive neural network algorithm and a simple correlation-based algorithm.  ... 
doi:10.1007/978-3-642-21498-1_26 fatcat:wo6jjl3nfbdfpf2nmlthqqudcq

Rethinking Convolutional Features in Correlation Filter Based Tracking [article]

Fang Liang, Wenjun Peng, Qinghao Liu, Haijin Wang
2019 arXiv   pre-print
Therefore, we propose a feature selection module to select more discriminative features for the trackers.  ...  In this paper, we revisit a hierarchical deep feature-based visual tracker and found that both the performance and efficiency of the deep tracker are limited by the poor feature quality.  ...  In most DCNN-DCF based works [7, 9, 28] , features employed for training correlation filters are directly extracted by a deep neural network pretrained on image classification dataset without any finetuning  ... 
arXiv:1912.12811v1 fatcat:uhz6azp6lfh4jiq2rm6eoohffm

A gradient boosting approach for optimal selection of bidding strategies in reservoir hydro [article]

Hans Ole Riddervold, Signe Riemer-Sørensen, Peter Szederjesi, Magnus Korpås
2020 arXiv   pre-print
neural networks).  ...  Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance.  ...  The criteria for selecting the best set of variables can therefore be based on the strategy gap, rather than the classification accuracy.  ... 
arXiv:2002.03941v1 fatcat:egzukvarsfdfrkqnsles2hwko4

Special Issue "Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis"

Mohand Djeziri, Marc Bendahan
2021 Processes  
Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional  ...  Induction motor is also considered in [3] which focus their study on the impact of the use of attribute selection methods such as ReliefF, correlation-based feature selection (CFS), and correlation and  ...  In [1] , a deep learning method associating wavelet transform for feature extraction under different frequencies and scales, and a convolutional neural network (CNN) for feature selection and fault classification  ... 
doi:10.3390/pr9030532 fatcat:5s4x6rkyj5hedaekapewutzujm

Multi-Objective Semi-Supervised Feature Selection and Model Selection Based on Pearson's Correlation Coefficient [chapter]

Frederico Coelho, Antonio Padua Braga, Michel Verleysen
2010 Lecture Notes in Computer Science  
This paper presents a Semi-Supervised Feature Selection Method based on a univariate relevance measure applied to a multiobjective approach of the problem.  ...  and, at the same time, to determine the optimal model of the neural network.  ...  Semi-Supervised Feature Selection Semi-supervised feature selection is based on the same principles of SSL.  ... 
doi:10.1007/978-3-642-16687-7_67 fatcat:izay33hhazc3rjpfwxx6iuqq24

Literature Review on Feature Selection Methods for High-Dimensional Data

D. Asir, S. Appavu, E. Jebamalar
2016 International Journal of Computer Applications  
for high-dimensional data, introduction to variable and feature selection, feature selection for classification.  ...  selection, ranking-based feature selection, wrapper-based feature selection, embedded-based feature selection, filter-based feature selection, hybrid feature selection, selecting feature from high-dimensional  ...  In this approach, two correlation measures are considered; one is feature-class correlation and another one is feature-feature correlation.  ... 
doi:10.5120/ijca2016908317 fatcat:fi3dkzxwnjgp5mop6xdr5luaze

Evaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks [article]

Ehsan Arbabi, Mohammad Bagher Shamsollahi
2017 arXiv   pre-print
Feature extraction, selection and classification are among the main matters of concerns in signal processing stage of BCI.  ...  We believe that the results can give an insight about a strategy for blind classification of brain signals in brain-computer interface.  ...  brain signals based on the selected features.  ... 
arXiv:1709.03252v2 fatcat:zkqvajh7dzardoufswf5ijesdq

A Review of Dimensionality Reduction Techniques for Efficient Computation

S. Velliangiri, S. Alagumuthukrishnan, S Iwin Thankumar joseph
2019 Procedia Computer Science  
In this paper presents most widely used feature extraction techniques such as EMD, PCA, and feature selection techniques such as correlation, LDA, forward selection have been analyzed based on high performance  ...  In this paper presents most widely used feature extraction techniques such as EMD, PCA, and feature selection techniques such as correlation, LDA, forward selection have been analyzed based on high performance  ...  Zou Characteristics of Feature Subset Selection Search systems select feature subset from original feature dataset based on the unique feature.  ... 
doi:10.1016/j.procs.2020.01.079 fatcat:2rrpxx3lfvghritucj6kct5sru

Robust and Efficient Classification for Underground Metal Target Using Dimensionality Reduction and Machine Learning

Yadong Wan, Tong Li, Peng Wang, Shihong Duan, Chao Zhang, Na Li
2021 IEEE Access  
) feature extraction method yielded the best performance in the material-based classification (accuracy:0.99) and the shape-based classification (accuracy:0.99).  ...  We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector  ...  FIGURE 10 . 10 The average accuracy (in columns) for different filter-based feature selection methods with different selected feature numbers (in rows) in shape-based classification.  ... 
doi:10.1109/access.2021.3049308 fatcat:mj2gzoqnafelha5hlfsh3v3s3y
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