"An Improved Random Forest Approach for Classification of Multi Variate Data"

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Book	Author(s)	Year	Publisher
Non-Uniform Random Variate Generation	Luc Devroye	1986	Springer New York
Automatic Nonuniform Random Variate Generation	Wolfgang Hörmann; Josef Leydold; Gerhard Derflinger	2004	Springer Berlin Heidelberg
Applied Matrix and Tensor Variate Data Analysis	Toshio Sakata	1st ed. 2016	Springer Japan
A forest classification for Canada	William Edward Durant Halliday	1937	J.O. Patenaude, printer to the King
Assessment of Biodiversity for Improved Forest Planning	Peter Bachmann; Michael Köhl; Risto Päivinen	1998	Springer Netherlands
An improved increment: core method for predicting growth of forest stands	S. R. Gevorkiantz	1948	U.S. Dept. of Agriculture, Forest Service, Lake States Forest Experiment Station
Limit Theorems for Multi-Indexed Sums of Random Variables	Oleg Klesov	2014	Springer Berlin Heidelberg
Multi-Sensor Data Fusion: An Introduction	H.B. Mitchell	2007	Springer Berlin Heidelberg
Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach	Shiyu Zhou	2021	John Wiley & Sons
Fuelwood from southern hardwoods: an opportunity for improved forest management	Daniel H. Sims	1981	U.S. Dept. of Agriculture, Forest Service, State & Private Forestry, Southeastern Area
Statistical Modelling of Survival Data with Random Effects: H-Likelihood Approach	Il Do Ha; Jong-Hyeon Jeong; Youngjo Lee	1st ed. 2017	Springer Nature Singapore
Design of Video Quality Metrics with Multi-Way Data Analysis: A data driven approach	Christian Keimel	1st ed. 2016	Springer Nature Singapore
An improved technique for taking hydraulic conductivity cores from forest soils	G. M. Aubertin	1969	Pa.
Approach to classification for students of librarianship	Derek Wilton Langridge	1973	Bingley
Classification Methods for Remotely Sensed Data	Paul Mather; Brandt Tso	2009	Routledge
Classification techniques for multivariate data analysis	Jin Ki Lee	1980	Naval Postgraduate School
Group problem solving: an improved managerial approach	Harvey J. Brightman	1988	Business Pub. Division, College of Business Administration, Georgia State University
Multi-objective Forest Planning	Timo Pukkala	2002	Springer Netherlands
Multi-scale Analysis for Random Quantum Systems with Interaction	Victor Chulaevsky; Yuri Suhov	2014	Springer New York
Guidelines for bio-physical land classification: for classification of forest lands and associated wildlands		1969	Dept. of Fisheries and Forestry, Forestry Service
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Improved Weighted Random Forest for Classification Problems	Mohsen Shahhosseini; Guiping Hu	Progress in Intelligent Decision Science: Proceeding of IDS 2020	Tofigh Allahviranloo; Soheil Salahshour; Nafiz Arica	1st ed. 2021	Springer International Publishing
Random forest for classification		Machine Learning with Scala Quick Start Guide: Leverage popular machine learning algorithms and techniques and implement them in Scala	Md. Rezaul Karim	2019	Packt Publishing
An Improved Classification Method Based on Random Forest for Medical Image	Niu Zhang; Haiwei Pan; Qilong Han; Xiaoning Feng	Foundations and Applications of Intelligent Systems: Proceedings of the Seventh International Conference on Intelligent Systems and Knowledge Engineering, Beijing, China, Dec 2012 (ISKE 2012)	Fuchun Sun; Tianrui Li; Hongbo Li	2014	Springer Berlin Heidelberg
Random Forest Classification		COMPUTER PROGRAMMING 4 Books In 1: Data Science, Hacking with Kali Linux, Computer Networking for Beginners, Python Programming	Jason Callaway	2020
Random forest classification		Mastering machine learning with R: advanced prediction, algorithms, and learning methods with R 3. x	Lesmeister, Cory	2017	Packt Publishing Limited
Random Forest Classification		Python for Beginners: Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python.	Campbell, Alex	2021
Random Forest Classification		Data Analysis from Scratch with Python: Step by Step Guide	Morgan, Peters	2016	Createspace Independent Publishing Platform
Random Forest for Imbalanced Classification		Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning		2020	Machine Learning Mastery
An Improved Multi-classification Algorithm for Imbalanced Online Public Opinion Data	Xige Dang; Xu Wu; Xiaqing Xie; Tianle Zhang	Artificial Intelligence and Security: 5th International Conference, ICAIS 2019, New York, NY, USA, July 26–28, 2019, Proceedings, Part IV	Xingming Sun; Zhaoqing Pan; Elisa Bertino	1st ed. 2019	Springer International Publishing
Tailoring Random Forest for Requirements Classification	Andreas Falkner; Gottfried Schenner; Alexander Schörghuber	Foundations of Intelligent Systems: 25th International Symposium, ISMIS 2020, Graz, Austria, September 23–25, 2020, Proceedings	Denis Helic; Gerhard Leitner; Martin Stettinger; Alexander Felfernig; Zbigniew W. Raś	1st ed. 2020	Springer International Publishing
Random Forest for Gene Selection and Microarray Data Classification	Kohbalan Moorthy; Mohd Saberi Mohamad	Knowledge Technology: Third Knowledge Technology Week, KTW 2011, Kajang, Malaysia, July 18-22, 2011. Revised Selected Papers	Dickson Lukose; Abdul Rahim Ahmad; Azizah Suliman	2012	Springer Berlin Heidelberg
Random Forest Classification and Regression Models for Literacy Data	Mayur Pandya; Jayaraman Valadi	Proceedings of International Joint Conference on Advances in Computational Intelligence: IJCACI 2021	Mohammad Shorif Uddin; Prashant Kumar Jamwal; Jagdish Chand Bansal	1st ed. 2022	Springer Nature Singapore
An Improved Approach to Ordinal Classification	Donghui Wang; Junhai Zhai; Hong Zhu; Xizhao Wang	Machine Learning and Cybernetics: 13th International Conference, Lanzhou, China, July 13-16, 2014. Proceedings	Xizhao Wang; Witold Pedrycz; Patrick Chan; Qiang He	2014	Springer Berlin Heidelberg
Random Variate Generation	Yahya E. Osais	Computer Simulation: A Foundational Approach Using Python	Yahya Esmail Osais	2017	Routledge
Random Variate Generation	Susmita Bandyopadhyay; Ranjan Bhattacharya	Discrete and Continuous Simulation: Theory and Practice	Susmita Bandyopadhyay; Ranjan Bhattacharya	2014	Routledge
Random Variate Generation		Wiley Encyclopedia of Operations Research and Management Science	James J. Cochran, Louis A. Cox Jr., Pinar Keskinocak, Jeffrey P. Kharoufeh, J. Cole Smith	2015	Wiley
Random Variate Generation		Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics	Paolo Brandimarte	2014	John Wiley & Sons
Random Variate Generation	Russell C. H. Cheng	Handbook of Simulation	Jerry Banks	1998	John Wiley & Sons
An Approach for Sentiment Analysis Using Gini Index with Random Forest Classification	Manpreet Kaur	Computational Vision and Bio-Inspired Computing: ICCVBIC 2019	S. Smys; João Manuel R. S. Tavares; Valentina Emilia Balas; Abdullah M. Iliyasu	1st ed. 2020	Springer International Publishing
RFT: An Industrial Data Classification Method Based on Random Forest	Caiyun Liu; Xuehong Chen; Yan Sun; Shuaifeng Yang; Jun Li	Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China, December 16–18, 2019, Proceedings, Part I	Huansheng Ning	1st ed. 2019	Springer Nature Singapore
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