Feature-Based Transfer Learning Based on Distribution Similarity

Zhong, Xiaofeng; Guo, Shize; Hong, Soondo; Gao, Liang; Xue, Di; Zhao, Nan

doi:10.1109/access.2018.2843773

Cited by 26 publications

(25 citation statements)

References 12 publications

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“…This helps in obtaining optimized latent subspaces S and T, as they are similar to each other. In addition, we can create a model using optimized latent subspace S and make predictions with optimized latent subspace T to obtain labeled data of target domain T [46].…”

Section: Optimizing Latent Subspace For Both Source and Target Domain Datamentioning

confidence: 99%

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PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

2021

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The number of studies on applying machine learning to cyber security has increased over the past few years. These studies, however, are facing difficulties with making themselves usable in the real world, mainly due to the lack of training data and reusability of a created model. While transfer learning seems like a solution to these problems, the number of studies in the field of intrusion detection is still insufficient. Therefore, this study proposes payload feature-based transfer learning as a solution to the lack of training data when applying machine learning to intrusion detection by using the knowledge from an already known domain. Firstly, it expands the extracting range of information from header to payload to accurately deliver the information by using an effective hybrid feature extraction method. Secondly, this study provides an improved optimization method for the extracted features to create a labeled dataset for a target domain. This proposal was validated on publicly available datasets, using three distinctive scenarios, and the results confirmed its usability in practice by increasing the accuracy of the training data created from the transfer learning by 30%, compared to that of the non-transfer learning method. In addition, we showed that this approach can help in identifying previously unknown attacks and reusing models from different domains.

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Section: Optimizing Latent Subspace For Both Source and Target Domain Datamentioning

confidence: 99%

“…For inter-domain optimization, two hyperparameters must be set: similarity parameter β [46] and the size of new feature space k. Therefore, we decided on the sensitivity of parameters through an experiment. First, we analyzed the sensitivity to similarity parameter β.…”

Section: Parameter Sensitivitymentioning

confidence: 99%

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

2021

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“…TL as an advanced variant of ML has attained great success in various fields, e.g., speech recognition [8,9], text mining [10], computer vision [11,12], and ubiquitous computing [13,14] over the last two decades. The existing TL approaches are categorized into following three main groups: (1) instance-based [15], (2) modelbased [16,17] and (3) feature-based [18,19] approaches.…”

Section: Related Workmentioning

confidence: 99%

Domain Adaptation via Bregman divergence minimization

2021

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In recent years, the Fisher linear discriminant analysis (FLDA) based classification models are among the most successful approaches and have shown effective performance in different classification tasks. However, when the learning data (source domain) have a different distribution compared with the testing data (target domain), the FLDA-based models may not work well, and the performance degrades, dramatically. To face this issue, we offer an optimal domain adaptation via Bregman divergence minimization (DAB) approach, in which the discriminative features of source and target domains are simultaneously learned via domain invariant representation. DAB is designed based on the constraints of FLDA, with the aim of the coupled marginal and conditional distribution shifts adaptation through Bregman divergence minimization. Thus, the resulting representation can show well functionality like FLDA and simultaneously discriminate across various classes, as well. Moreover, our proposed approach can be easily kernelized to deal with nonlinear tasks. Different experiments on various benchmark datasets demonstrate that our DAB can constructively face with the cross domain divergence and outperforms other novel state-of-the-art domain adaptation approaches in crossdistribution domains.

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“…Auxiliary source domain often has different distribution from target domain that makes traditional machine learning algorithms useless, since they assume that the source and target data are under the same distributions. In such a circumstance, transfer learning methods are used to reduce the distribution divergence where they can be divided into following three categories: (a) instance-based methods [ 47 ], which reuse the samples from source domain according to re-weighting techniques, (b) feature-based methods [ 46 , 54 ], which learn the subspace with shared features to represent the source and target data under common conditions or perform distribution alignment to minimize the marginal or conditional distribution divergences between domains, and (c) model-based methods [ 31 ], which transfer parameters of source model to improve the performance of target model.…”

Section: Introductionmentioning

confidence: 99%

Iterative joint classifier and domain adaptation for visual transfer learning

Saray

Tahmoresnezhad

2021

Int. J. Mach. Learn. & Cyber.

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Current available supervised classifiers cannot generalize across various domains due to distribution mismatch among them. Domain adaptation and transfer learning algorithms are proposed to tackle domain shift problem that originates from different data collection conditions. In this paper, we propose a transfer learning framework called iterative joint classifier and domain adaptation for visual transfer learning (ICDAV), which utilizes the balanced maximum mean discrepancy to better transfer knowledge across domains. Also, for learning a robust classifier against domain shift, a set of graph manifold regularizer and modified joint probability maximum mean discrepancy are simultaneously exploited to capture the domain structures and adapt the distribution of projected samples during the model learning process. Variety of experiments on several public datasets indicates that our approach achieves remarkable performance on visual domain adaptation and transfer learning tasks.

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Feature-Based Transfer Learning Based on Distribution Similarity

Cited by 26 publications

References 12 publications

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

Domain Adaptation via Bregman divergence minimization

Iterative joint classifier and domain adaptation for visual transfer learning

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