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Gene selection with guided regularized random forest

Pattern recognition, 2013-12, Vol.46 (12), p.3483-3489

2013

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Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

NeuroImage (Orlando, Fla.), 2015-03, Vol.108, p.214-224

2015

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Interpreting tree ensembles with inTrees

International journal of data science and analytics, 2019-06, Vol.7 (4), p.277-287

Deng, Houtao

2019

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CBC: An associative classifier with a small number of rules

Decision Support Systems, 2014-03, Vol.59, p.163-170

2014

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Integrative analysis of the connectivity and gene expression atlases in the mouse brain

NeuroImage (Orlando, Fla.), 2014-01, Vol.84, p.245-253

2014

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System Monitoring with Real-Time Contrasts

Journal of quality technology, 2012-01, Vol.44 (1), p.9-27

2012

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Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm

Journal of Spectroscopy, 2017-01, Vol.2017, p.1-8

2017

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A time series forest for classification and feature extraction

Information sciences, 2013-08, Vol.239, p.142-153

2013

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Feature selection via regularized trees

The 2012 International Joint Conference on Neural Networks (IJCNN), 2012, p.1-8

2012

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IEEE_Xplore

A distance-based control chart for monitoring multivariate processes using support vector machines

Annals of operations research, 2018-04, Vol.263 (1-2), p.191-207

2018

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Bias of Importance Measures for Multi-valued Attributes and Solutions

Artificial Neural Networks and Machine Learning – ICANN 2011, p.293-300

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Diagnostic monitoring of high-dimensional networked systems via a LASSO-BN formulation

IIE transactions, 2017-09, Vol.49 (9), p.874-884

2017

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SMT: Sparse multivariate tree

Statistical analysis and data mining, 2014-02, Vol.7 (1), p.53-69

2014

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System Complexity Reduction via Feature Selection

ProQuest LLC, 2011

Deng, Houtao

2011

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ProQuest

Leveraging Elastic Demand for Forecasting

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019, p.268-271

2019

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Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction

Environmental pollution (1987), 2021-12, Vol.291, p.118153-118153, Article 118153

2021

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Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

ACS ES&T water, 2021-08, Vol.1 (8), p.1676-1685

2021

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Gene selection with guided regularized random forest

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

Interpreting tree ensembles with inTrees

CBC: An associative classifier with a small number of rules

Integrative analysis of the connectivity and gene expression atlases in the mouse brain

System Monitoring with Real-Time Contrasts

Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm

A time series forest for classification and feature extraction

Feature selection via regularized trees

A distance-based control chart for monitoring multivariate processes using support vector machines

Bias of Importance Measures for Multi-valued Attributes and Solutions

Diagnostic monitoring of high-dimensional networked systems via a LASSO-BN formulation

SMT: Sparse multivariate tree

System Complexity Reduction via Feature Selection

Leveraging Elastic Demand for Forecasting

Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

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