Quantifying Feature Importance for Detecting Depression using Random Forest
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[1] Songyot Nakariyakul,et al. High-dimensional hybrid feature selection using interaction information-guided search , 2018, Knowl. Based Syst..
[2] Mike Conway,et al. Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health , 2016, PEOPLES@COLING.
[3] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[4] Paola Zuccolotto,et al. Variable Selection Using Random Forests , 2006 .
[5] Bin Hu,et al. Study on Feature Selection Methods for Depression Detection Using Three-Electrode EEG Data , 2018, Interdisciplinary Sciences: Computational Life Sciences.
[6] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[7] Celine Vens,et al. Random Forest Based Feature Induction , 2011, 2011 IEEE 11th International Conference on Data Mining.
[8] A. Mitchell,et al. Clinical diagnosis of depression in primary care: a meta-analysis , 2009, The Lancet.
[9] S. Koteeswaran,et al. Feature Selection using Random Forest Method for Sentiment Analysis , 2016 .
[10] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[11] Diana Inkpen,et al. Monitoring Tweets for Depression to Detect At-risk Users , 2017, CLPsych@ACL.
[12] Tianwei Yu,et al. A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification , 2018, Scientific Reports.
[13] Mourad Ykhlef,et al. Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features , 2020, IEICE Trans. Inf. Syst..
[14] Samina Khalid,et al. A survey of feature selection and feature extraction techniques in machine learning , 2014, 2014 Science and Information Conference.
[15] Dhruba Kumar Bhattacharyya,et al. An effective ensemble classification framework using random forests and a correlation based feature selection technique , 2017, Trans. GIS.
[16] Moin Nadeem,et al. Identifying Depression on Twitter , 2016, ArXiv.
[17] Arkaprabha Sau,et al. Predicting anxiety and depression in elderly patients using machine learning technology , 2017 .
[18] Shahin Ara Begum,et al. A Survey on Case-based Reasoning in Medicine , 2016 .
[19] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[20] Ramón Díaz-Uriarte,et al. Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.
[21] Sharath Chandra Guntuku,et al. Detecting depression and mental illness on social media: an integrative review , 2017, Current Opinion in Behavioral Sciences.
[22] Christopher M. Danforth,et al. Forecasting the onset and course of mental illness with Twitter data , 2016, Scientific Reports.
[23] Guido Caldarelli,et al. Echo Chambers: Emotional Contagion and Group Polarization on Facebook , 2016, Scientific Reports.
[24] Eric Horvitz,et al. Predicting Depression via Social Media , 2013, ICWSM.
[25] Adelina Tang,et al. A Qualitative Evaluation of Random Forest Feature Learning , 2014, SCDM.
[26] Kellie J. Archer,et al. Empirical characterization of random forest variable importance measures , 2008, Comput. Stat. Data Anal..
[27] Arunkumar Chinnaswamy,et al. Hybrid Feature Selection Using Correlation Coefficient and Particle Swarm Optimization on Microarray Gene Expression Data , 2015, IBICA.
[28] J. Rabinowitz,et al. Post-traumatic stress disorder in primary-care settings: prevalence and physicians' detection , 2001, Psychological Medicine.
[29] A. Boulesteix,et al. Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.
[30] Víctor M. Prieto,et al. Twitter: A Good Place to Detect Health Conditions , 2014, PloS one.
[31] Tat-Seng Chua,et al. Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution , 2017, IJCAI.