Semi-supervised Learning with Ensemble Learning and Graph Sharpening

2008 ◽  
pp. 172-179 ◽  
Author(s):  
Inae Choi ◽  
Hyunjung Shin
Keyword(s):  
Supervised Learning ◽  
Ensemble Learning

scholarly journals The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Gulshan Kumar ◽  
Krishan Kumar
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Classification Performance ◽  
Intrusion Detection Systems ◽  
Future Directions ◽  
Comprehensive Review ◽  
Detection Systems ◽  
Combination Methods

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).

“In-Network Ensemble”: Deep Ensemble Learning with Diversified Knowledge Distillation

2021 ◽  
Vol 12 (5) ◽  
pp. 1-19
Author(s):  
Xingjian Li ◽  
Haoyi Xiong ◽  
Zeyu Chen ◽  
Jun Huan ◽  
Cheng-Zhong Xu ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Transfer Learning ◽  
Ensemble Learning ◽  
Large Scale ◽  
Ensemble Classifier ◽  
Network Architectures ◽  
Deep Convolutional Neural Networks ◽  
Knowledge Distillation ◽  
Real World Datasets ◽  
Improved Accuracy

Ensemble learning is a widely used technique to train deep convolutional neural networks (CNNs) for improved robustness and accuracy. While existing algorithms usually first train multiple diversified networks and then assemble these networks as an aggregated classifier, we propose a novel learning paradigm, namely, “In-Network Ensemble” ( INE ) that incorporates the diversity of multiple models through training a SINGLE deep neural network. Specifically, INE segments the outputs of the CNN into multiple independent classifiers, where each classifier is further fine-tuned with better accuracy through a so-called diversified knowledge distillation process . We then aggregate the fine-tuned independent classifiers using an Averaging-and-Softmax operator to obtain the final ensemble classifier. Note that, in the supervised learning settings, INE starts the CNN training from random, while, under the transfer learning settings, it also could start with a pre-trained model to incorporate the knowledge learned from additional datasets. Extensive experiments have been done using eight large-scale real-world datasets, including CIFAR, ImageNet, and Stanford Cars, among others, as well as common deep network architectures such as VGG, ResNet, and Wide ResNet. We have evaluated the method under two tasks: supervised learning and transfer learning. The results show that INE outperforms the state-of-the-art algorithms for deep ensemble learning with improved accuracy.

A Novel Diversity-Based Semi-Supervised Learning Framework with Related Theoretical Analysis

2015 ◽  
Vol 24 (03) ◽  
pp. 1550011
Author(s):  
Zhen Jiang ◽  
Yong-Zhao Zhan
Keyword(s):  
Theoretical Analysis ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Upper Bound ◽  
Learning Algorithms ◽  
Voting Systems ◽  
Generalization Error ◽  
Learning Framework ◽  
Bagging Method ◽  
Better Than

We present a new co-training style framework and combine it with ensemble learning to further improve the generalization ability. By employing different strategies to combine co-training with ensemble learning, two learning algorithms, Sequential Ensemble Co-Learning (SECL) and Parallel Ensemble Co-Learning (PECL) are developed. Furthermore, we propose a weighted bagging method in PECL to generate an ensemble of diverse classifiers at the end of co-training. Finally, based on the voting margin, an upper bound on the generalization error of multi-classifier voting systems is given in the presence of both classification noise and distribution noise. Experimental results on six datasets show that our method performs better than other compared algorithms.

Semi-supervised learning using frequent itemset and ensemble learning for SMS classification

2015 ◽  
Vol 42 (3) ◽  
pp. 1065-1073 ◽  
Author(s):  
Ishtiaq Ahmed ◽  
Rahman Ali ◽  
Donghai Guan ◽  
Young-Koo Lee ◽  
Sungyoung Lee ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Ensemble Learning ◽  
Frequent Itemset

scholarly journals Quantum Markovianity as a supervised learning task

2018 ◽  
Vol 16 (08) ◽  
pp. 1840010 ◽  
Author(s):  
Sally Shrapnel ◽  
Fabio Costa ◽  
Gerard Milburn
Keyword(s):  
Random Forest ◽  
Predictive Model ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Learning Algorithms ◽  
Learning Task ◽  
Learning Method ◽  
Supervised Learning Algorithms ◽  
Promising Line

Supervised learning algorithms take as input a set of labeled examples and return as output a predictive model. Such models are used to estimate labels for future, previously unseen examples, drawn from the same generating distribution. In this paper, we investigate the possibility of using supervised learning to estimate the dimension of a non-Markovian quantum environment. Our approach uses an ensemble learning method, the Random Forest Regressor, applied to classically simulated datasets. Our results indicate this is a promising line of research.

scholarly journals Predicting colorectal cancer recurrence by utilizing multiple-view multiple-learner supervised learning.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 635-635 ◽  
Author(s):  
Jason Castellanos ◽  
Qi Liu ◽  
R. Daniel Beauchamp ◽  
Bing Zhang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Clinical Data ◽  
Predictive Models ◽  
The United States ◽  
Linear Classifiers ◽  
Multiple View ◽  
Increased Risk

635 Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality in the United States. A key therapeutic dilemma in the treatment of CRC is whether patients with stage II and stage III disease require adjuvant chemotherapy after surgical resection. Attempts to improve identification of patients at increased risk of recurrence have yielded many predictive models based on gene expression data, but none are FDA approved and none are used in standard clinical practice. To improve recurrence prediction, we utilize a machine learning approach to predict recurrence status at 3 years after diagnosis. Methods: A dataset was curated from six publically available microarray datasets, and multiple views were generated to include information from non-tumor tissue gene expression patterns, gene set structure, protein-protein interaction network structure, previously curated molecular signatures, and identified tumor suppressor/driver mutations. These views were used to train a diverse pool of base learners using 10x 10-fold cross-validation. Stacked generalization was used to train an ensemble model, also known as a meta-learner, from the predictions of these base learners. Results: The performance of microarray trained models was significantly better compared to models trained on clinical data (Paired Wilcoxon signed rank test, p = 1.49 x 10-8), demonstrating that molecular data predicts recurrence significantly better than basic clinical data. Review of the model training performances revealed that non-linear classifiers often outperform linear classifiers, and that ensemble methods can also enhance performance. We also demonstrate the feasibility of the multiple-view multiple learner (MVML) supervised learning framework to generate and integrate predictions across a diverse set of learners, with the performance of the meta-learner exceeding or matching that of the best base learners across all performance metrics. Conclusions: This work represents the first effort to use ensemble learning to predict CRC recurrence and highlights the promise of ensemble learning to improve the performance of predictive models in order to realize the goals of precision medicine.

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