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Correlation-based Feature Selection Strategy in Neural Classification
2006
Sixth International Conference on Intelligent Systems Design and Applications
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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
2012
International Journal of Machine Learning and Computing
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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
2020
Computers Materials & Continua
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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
2020
Indian Journal of Science and Technology
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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
2022
Computers Materials & Continua
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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
2017
Naukovì Vìstì Nacìonalʹnogo Tehnìčnogo Unìversitetu Ukraïni Kiïvsʹkij Polìtehnìčnij Institut
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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]
2011
Lecture Notes in Computer Science
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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]
2019
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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"
2021
Processes
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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]
2010
Lecture Notes in Computer Science
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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
2016
International Journal of Computer Applications
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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]
2017
arXiv
pre-print
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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
2019
Procedia Computer Science
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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
2021
IEEE Access
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) 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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