Ensemble based classifiers using dictionary learning

Tuysuzoglu, Goksu; Moarref, Nazanin; Yaslan, Yusuf

doi:10.1109/iwssip.2016.7801373

Cited by 10 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority voting grouping technique is used in [17,18]. In [17], the bagging method of ensemble is used with REPTree as base classifier for intrusion detection systems, and compared to other traditional machine learning techniques.…”

Section: Bagging Learningmentioning

confidence: 99%

“…It is shown that the ensemble bagging method achieved high classification accuracy by employing NSL_KDD dataset. The authors of [18] proposed to use dictionary learning with random subspace and bagging methods, and introduced Random Subspace Dictionary Learning (RDL) and Bagging Dictionary Learning (BDL) algorithms. Their experimental analysis concluded that ensemble based dictionary learning methods performed better than that of single dictionary learning.…”

Section: Bagging Learningmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

2020

View full text Add to dashboard Cite

Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when there is no systematic news verification process, which is the case for traditional media. Detecting false information, for example, detection of rumors is a non-trivial task, given the fast-paced social media environment. In this work, we proposed an ensemble model, which performs majority-voting scheme on a collection of predictions of neural networks using time-series vector representation of Twitter data for fast detection of rumors. Experimental results show that proposed neural network models outperformed classical machine learning models in terms of micro F1 score. When compared to our previous works the improvements are 12.5% and 7.9%, respectively.

show abstract

Section: Bagging Learningmentioning

confidence: 99%

Section: Bagging Learningmentioning

confidence: 99%

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

2020

View full text Add to dashboard Cite

show abstract

“…Artificial neural networks are also used in ensemble learning as weak classifiers. SR has been used as a base classifier in random subspace (RS) and bagging ensemble methods in the area of signal processing [32], [33]. CRC-bagging subsequently proved to be effective in hyperspectral classification [34].…”

Section: Introductionmentioning

confidence: 99%

Diversity-Driven Multikernel Collaborative Representation Ensemble for Hyperspectral Image Classification

Lü

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Recently, kernel collaborative representation classification (KCRC) has shown its outstanding performance in dealing with the problem of linear inseparability in hyperspectral remote sensing image classification. Meanwhile, ensemble learning has attracted great attention in improving the performance of a single classifier. Aiming at the limitation of a single classifier, bagging algorithm based on KCRC(KCRC-bagging) is presented in this article. The KCRC-bagging method uses bootstrap to increase the diversity of base classifiers, thus improving the classification accuracy and generalization performance. In order to reduce the scale of ensemble, a diversity-driven multikernel collaborative representation classifier ensemble approach (DIV-KCRC) is proposed. DIV-KCRC verifies the effectiveness of the representation classifier with the pair of diversity measures, and classifiers with high accuracy and diversity are selected to improve the classification performance and efficiency of the ensemble system. Three real hyperspectral data sets were applied to prove the validity of the proposed method. The experimental results demonstrate that both KCRC-bagging and DIV-KCRC can yield better classification performance than their corresponding base classifiers. In particular, DIV-KCRC provides more reliable classification results than KCRC-bagging.

show abstract