A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

Tarun Rao PhD

Contact

T.V.Rajinikanth

Contact

Dayananda Sagar College of Engineering

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

Article Fingerprint

ReserarchID

CSTSDEQB1PD

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting Banner

AI TAKEAWAY

Connecting with the Eternal Ground

References

50 Cites in Article

Reference Format

Till Rumpf,Christoph Römer,Martin Weis,Markus Sökefeld,Roland Gerhards,Lutz Plümer (2012). Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine.
Jing Hu,Daoliang Li,Qingling Duan,Yueqi Han,Guifen Chen,Xiuli Si (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision.
L Naidoo,M Cho,R Mathieu,G Asner (2012). Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment.
Maysam Abedi,Gholam-Hossain Norouzi,Abbas Bahroudi (2012). Support vector machine for multiclassification of mineral prospectivity areas.
D Ruano-Ordás,J Fdez-Glez,F Fdez-Riverola,J Méndez (2013). Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks.
N Rahim,Paulraj M.P.,A Adom (2013). Adaptive Boosting with SVM Classifier for Moving Vehicle Classification.
Konstantinos Topouzelis,Apostolos Psyllos (2012). Oil spill feature selection and classification using decision tree forest on SAR image data.
Yang Shao,Ross Lunetta (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points.
Paul Bosch,Julio López,Héctor Ramírez,Hugo Robotham (2013). Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile.
Hongji Lin,Han Lin,Weibin Chen (2011). Study on Recognition of Bird Species in Minjiang River Estuary Wetland.
Rafael Pino-Mejías,María Cubiles-De-La-Vega,María Anaya-Romero,Antonio Pascual-Acosta,Antonio Jordán-López,Nicolás Bellinfante-Crocci (2010). Predicting the potential habitat of oaks with data mining models and the R system.
S Jeyanthi (2007). An Independent Study Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Business Administration Marketing.
G Yugal Kumar,Sahoo (2012). Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA, I.
M Kumar,M (2010). A hybrid SVM based decision tree.
N Rajasekhar,S Babu,T Rajinikanth (2012). Magnetic resonance brain images classification using linear kernel based Support Vector Machine.
V Rodríguez-Galiano,F Abarca-Hernández,B Ghimire,M Chica-Olmo,P Atkinson,C Jeganathan (2011). Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest.
Reda Elbasiony,Elsayed Sallam,Tarek Eltobely,Mahmoud Fahmy (2013). A hybrid network intrusion detection framework based on random forests and weighted k-means.
Gonzalo Martínez-Muñoz,Alberto Suárez (2007). Using boosting to prune bagging ensembles.
Chun-Xia Zhang,Jiang-She Zhang,Gai-Ying Zhang (2008). An efficient modified boosting method for solving classification problems.
F Löw,U Michel,S Dech,C Conrad (2013). Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines.
Miao Liu,Mingjun Wang,Jun Wang,Duo Li (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar.
Ching-Chiang Yeh,Der-Jang Chi,Yi-Rong Lin (2013). Going-concern prediction using hybrid random forests and rough set approach.
Hsun-Jung Cho,Ming-Te Tseng (2013). A support vector machine approach to CMOS-based radar signal processing for vehicle classification and speed estimation.
Hong Lam,Rajprasad Lee,Rajkumar,Hung Lai,Chin Lo,Dino Heng Wan,Isa (2013). Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach.
David Meyer (2012). Support Vector Machines The Interface to lib svm in package e1071.
Xiaowei Yang,Qiaozhen Yu,Lifang He,Tengjiao Guo (2013). The one-against-all partition based binary tree support vector machine algorithms for multi-class classification.
Steve Gunn (1998). Support Vector Machines for Classification and Regression.
Asdrúbal López,Chau,Xiaoou Li,Wen Yu (2013). Convex and concave hulls for classification with support vector machine.
Xinjun Peng,Yifei Wang,Dong Xu (2013). Structural twin parametric-margin support vector machine for binary classification.
Adnan Idris,Muhammad Rizwan,Asifullah Khan (2012). Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies.
Elias Zintzaras,Axel Kowald (2010). Forest classification trees and forest support vector machines algorithms: Demonstration using microarray data.
Ching-Chiang Yeh,Fengyi Lin,Chih-Yu Hsu (2012). A hybrid KMV model, random forests and rough set theory approach for credit rating.
Sebastian Björnwaske,Carsten Vander Linden,Benjamin Oldenburg,Andreas Jakimow,Patrick Rabe,Hostert (2012). imageRF -A user-oriented implementation for remote sensing image analysis with Random Forests.
Miao Liu,Mingjun Wang,Jun Wang,Duo Li (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar.
Robin Genuer,Jean-Michel Poggi,Christine Tuleau-Malot (2010). Variable selection using random forests.
Katherine Gray,Paul Aljabar,Rolf Heckemann,Alexander Hammers,Daniel Rueckert (2013). Random forest-based similarity measures for multi-modal classification of Alzheimer's disease.
Meng Xiao,Hong Yan,Jinzhong Song,Yuzhou Yang,Xianglin Yang (2013). Sleep stages classification based on heart rate variability and random forest.
Akin Özçift (2011). Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.
Simone Borra,Agostino Di Ciaccio (2002). Improving nonparametric regression methods by bagging and boosting.
Imed Zitouni,Hong-Kwang Jeff Kuo,Chin-Hui Lee (2003). Boosting and combination of classifiers for natural language call routing systems.
Jafar Tanha,Maarten Van Someren,Hamideh Afsarmanesh (2013). Boosting for multiclass semi-supervised learning.
Tong Xiao,Jingbo Zhu,Tongran Liu (2013). Bagging and Boosting statistical machine translation systems.
Tansel Özyer,Reda Alhajj,Ken Barker (2007). Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule prescreening.
Jakkrit Techo,Cholwich Nattee,Thanaruk Theeramunkong (2012). Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition.
Chun-Xia Zhang,Jiang-She Zhang (2008). A local boosting algorithm for solving classification problems.
Shaban Shataeea,Holger Weinaker,Manoucher Babanejad (2011). Plot-level Forest Volume Estimation Using Airborne Laser Scanner and TM Data, Comparison of Boosting and Random Forest Tree Regression Algorithms.
Qboost Song Feng Zheng (2012). Predicting quantiles with boosting for regression and binary classification.
L Kuncheva,M Skurichina,R Duin (2002). An experimental study on diversity for bagging and boosting with linear classifiers.
D Lu,Q Weng (2007). A survey of image classification methods and techniques for improving classification performance.
Team} {r Core (2013). R: A Language and Environment for Statistical Computing.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tarun Rao. 2014. \u201cA Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 14 Issue C1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

boosting Classification Data mining random forest remote sensed data support vector machine

Submission ReceivedDecember 7, 2013
Peer Review Double Blind
Handling Editor
Accepted January 5, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

May 14, 2014

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 102

Country: Unknown

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Tarun Rao, T.V.Rajinikanth (PhD/Dr. count: 0)

View Count (all-time): 302

Total Views (Real + Logic): 8903

Total Downloads (simulated): 2420

Publish Date: 2014 05, Wed

Monthly Totals (Real + Logic):

Month 1: 56 views
Month 2: 49 views
Month 3: 33 views
Month 4: 46 views
Month 5: 36 views
Month 6: 41 views
Month 7: 41 views
Month 8: 36 views
Month 9: 45 views
Month 10: 57 views
Month 11: 47 views
Month 12: 33 views
Month 13: 31 views
Month 14: 26 views
Month 15: 21 views
Month 16: 45 views
Month 17: 26 views
Month 18: 27 views
Month 19: 21 views
Month 20: 34 views
Month 21: 34 views
Month 22: 40 views
Month 23: 44 views
Month 24: 17 views
Month 25: 32 views
Month 26: 33 views
Month 27: 45 views
Month 28: 32 views
Month 29: 38 views
Month 30: 30 views
Month 31: 35 views
Month 32: 32 views
Month 33: 29 views
Month 34: 18 views
Month 35: 32 views
Month 36: 21 views
Month 37: 20 views
Month 38: 24 views
Month 39: 46 views
Month 40: 11 views
Month 41: 30 views
Month 42: 13 views
Month 43: 36 views
Month 44: 22 views
Month 45: 19 views
Month 46: 33 views
Month 47: 15 views
Month 48: 46 views
Month 49: 37 views
Month 50: 27 views
Month 51: 29 views
Month 52: 13 views
Month 53: 34 views
Month 54: 43 views
Month 55: 37 views
Month 56: 40 views
Month 57: 43 views
Month 58: 42 views
Month 59: 16 views
Month 60: 17 views
Month 61: 43 views
Month 62: 23 views
Month 63: 36 views
Month 64: 38 views
Month 65: 30 views
Month 66: 29 views
Month 67: 22 views
Month 68: 20 views
Month 69: 41 views
Month 70: 29 views
Month 71: 18 views
Month 72: 41 views
Month 73: 20 views
Month 74: 30 views
Month 75: 40 views
Month 76: 30 views
Month 77: 27 views
Month 78: 22 views
Month 79: 40 views
Month 80: 25 views
Month 81: 37 views
Month 82: 34 views
Month 83: 40 views
Month 84: 19 views
Month 85: 24 views
Month 86: 44 views
Month 87: 26 views
Month 88: 29 views
Month 89: 26 views
Month 90: 29 views
Month 91: 17 views
Month 92: 17 views
Month 93: 36 views
Month 94: 30 views
Month 95: 38 views
Month 96: 20 views
Month 97: 26 views
Month 98: 40 views
Month 99: 44 views
Month 100: 43 views
Month 101: 19 views
Month 102: 15 views
Month 103: 32 views
Month 104: 24 views
Month 105: 39 views
Month 106: 44 views
Month 107: 34 views
Month 108: 43 views
Month 109: 26 views
Month 110: 37 views
Month 111: 40 views
Month 112: 42 views
Month 113: 19 views
Month 114: 16 views
Month 115: 19 views
Month 116: 18 views
Month 117: 43 views
Month 118: 15 views
Month 119: 42 views
Month 120: 38 views
Month 121: 43 views
Month 122: 50 views
Month 123: 20 views
Month 124: 21 views
Month 125: 40 views
Month 126: 42 views
Month 127: 21 views
Month 128: 38 views
Month 129: 18 views
Month 130: 24 views
Month 131: 41 views
Month 132: 34 views
Month 133: 28 views
Month 134: 44 views
Month 135: 41 views
Month 136: 37 views
Month 137: 30 views
Month 138: 40 views
Month 139: 36 views
Month 140: 12 views
Month 141: 35 views
Month 142: 64 views
Month 143: 44 views
Month 144: 46 views
Month 145: 46 views

Total Views: 8903

Total Downloads: 2420

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]