Constructing support vector machine ensemble

By Hyun-Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, Sung Yang Bang. Pattern Recognition, 2003

517 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/117f9d46af6858c8b6ad5dd76b49a9d3c4b41fa9

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A Mixed-Integer Programming Approach to Multi-Class Data Classification Problem by (2005, methodology): “To overcome above problems, many variants of SVM are suggested including the use of SVM ensemble with bagging or boosting rather than using a single SVM [29].”
Ensemble methods and partial least squares regression (2004, methodology): “Keywords ensemble methods; bootstrap aggregating (bagging); data augmentation; noise addition; partial least squares regression (PLSR)”
Ensemble approaches for regression: A survey (2012, methodology): “Base learners Bagging [Breiman 1996a] DT & ANN Random subspace [Ho 1998] DT Ensemble feature selection [Opitz 1999] ANN Using diversity in [Zenobi and Cunningham 2001] ANN preparing ensembles Nearest Neighbor Ensemble [Domeniconi and Yan 2004] KNN BEN Bootstrap Ensemble with Noise, or [Raviv ”
Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings (2009, background): “optimal hyperplane in (1) is a quadratic programming (QP) problem that can be solved by constructing a Lagrangian and transforming into the following dual problem [43]”
SVM and SVM Ensembles in Breast Cancer Prediction (2017, methodology): “Then, the k classifiers are aggregated via an appropriate combination method, such as majority voting [32].”
Fraud detection system: A survey (2016, methodology): “Furthermore, Kim et al. (2003) used feature selection and extraction to discover indicators corresponding to changes in behavioral indicatives of fraud.”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, methodology): “Max Voting: The ﬁrst and most popular voting method is the max voting (Kim et al., 2003) often, often known as majority voting or hard voting.”
A Comprehensive Survey of Data Mining-based Fraud Detection Research (2010, methodology): “Regrettably, some recent studies on credit card transactional fraud (Chen et al , 2004) and telecommunications superimposed fraud (Kim et al , 2003) still aim to only maximise accuracy.”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, methodology): “We expect the results of this MCS to show clearer trends than those reported in previous studies that use bagging or boosting [27]–[29].”
Machine learning for Internet of Things data analysis: A survey (2017, background): “SVMs are useful in many real-world applications such as hand-written character recognition [105], image classi ﬁ cation [106], and protein classi ﬁ cation [107].”
A survey of image classification methods and techniques for improving classification performance (2007, unknown): “These techniques have been used in decision trees (Friedl et al. 1999, DeFries and Chan 2000, Lawrence et al. 2004) and a support vector machine (Kim et al. 2003) to enhance classifications.”
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, background): “However, in recent years it has been shown that Ensemble classification is even more e↵ective when strong base classifiers are integrated (e.g., [Kim et al. 2003]).”
An ensemble system for automatic sleep stage classification using single channel EEG signal (2012, methodology): “For the selection of proper Ĉ of the SVM model and g value for the RBF kernel function, the values of Ĉ and g were varied iteratively through grid-search process [61], in each of the iterations the average classification performance was obtained by CV technique.”