Lifelong Learning for Sentiment Classification

Chen, Zhiyuan; Ma, Nianzu; Liu, Bing

doi:10.3115/v1/p15-2123

Cited by 169 publications

(134 citation statements)

References 24 publications

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“…Consequently, the concept of lifelong learning can hold realistic significance in long-term real-world applications. The concept of lifelong machine learning [106,265,266] is directed towards the construction of a model that can perform the retraining process repeatedly for the learning of new emerging patterns related to each behaviour. The model should be able to adapt to and learn from new environments continuously [106,265,266].…”

Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

“…The concept of lifelong machine learning [106,265,266] is directed towards the construction of a model that can perform the retraining process repeatedly for the learning of new emerging patterns related to each behaviour. The model should be able to adapt to and learn from new environments continuously [106,265,266]. Researchers have reported that the further they trained the algorithm with the latest features of known DDoS attacks, the more they improved the detection probabilities for known and unknown DDoS attacks.…”

Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

838

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

Section: ) Lifelong Learning For Learning Iot Threatsmentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

838

333

View full text Add to dashboard Cite

show abstract

“…Both concepts focus on the continuous learning processes for evolving tasks. However, sequential/online transfer learning emphasizes on how to improve the target domain performance (without sufficient target training data), but lifelong learning tries to improve the future target task (with sufficient target training data) as well as all the past tasks [28]. Also, the lifelong learning can be seen as incremental/online multi-task learning.…”

Section: Sequential/online Labelled Target Datamentioning

confidence: 99%

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition

Zhang

Ogunbona

et al. 2019

ACM Comput. Surv.

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This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied.This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly.

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“…Never-ending learning is a rather general setup that defines how machines can learn like humans to transfer experience to different tasks in a self-supervised manner, and has been realized in a system for accumulating beliefs by reading continuously from the web [9]. Life-long learning [10], [11], on the other hand, considers feeding the machines with a sequence of tasks with the hope of improving the performance on the next task in the sequence. The setup is similar to our thought but has been realized on only sentiment classification tasks [10].…”

Section: Introductionmentioning

confidence: 99%

Can Active Learning Experience Be Transferred?

Chu

Lin

2016

2016 IEEE 16th International Conference on Data Mining (ICDM)

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Active learning is an important machine learning problem in reducing the human labeling effort. Current active learning strategies are designed from human knowledge, and are applied on each dataset in an immutable manner. In other words, experience about the usefulness of strategies cannot be updated and transferred to improve active learning on other datasets. This paper initiates a pioneering study on whether active learning experience can be transferred. We first propose a novel active learning model that linearly aggregates existing strategies. The linear weights can then be used to represent the active learning experience. We equip the model with the popular linear upperconfidence-bound (LinUCB) algorithm for contextual bandit to update the weights. Finally, we extend our model to transfer the experience across datasets with the technique of biased regularization. Empirical studies demonstrate that the learned experience not only is competitive with existing strategies on most single datasets, but also can be transferred across datasets to improve the performance on future learning tasks.

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Lifelong Learning for Sentiment Classification

Cited by 169 publications

References 24 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition

Can Active Learning Experience Be Transferred?

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