Incremental semi-supervised learning on streaming data

Li, Yanchao; Wang, Yongli; Liu, Qi; Bi, Chuan‐Xing; Jiang, Xiaohui; Sun, Shurong

doi:10.1016/j.patcog.2018.11.006

Cited by 45 publications

(29 citation statements)

References 22 publications

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“…Our distributed architecture is inspired by a method based on Incremental semi-supervised learning on streaming data for video classification which consists of 3 layers [32]. The first layer learns features from the incoming streaming data, the third layer regularises the network by building similarity constraints.…”

Section: A Distributed Architecturementioning

confidence: 99%

“…So we will not consider that feature as new and the value returned to the Algorithm is 0. We set the minimum distance by cosine distance measure, which is used to identify the similarities of feature characteristics by calculating the normalized inner product [32]. If ever the cosine distance is higher than the minimum value calculated, then it is considered as a new feature and added as a new filter to the network.…”

Section: B Bridge Network: Comparison Phasementioning

confidence: 99%

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Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

et al. 2021

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Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results. INDEX TERMSDeep learning, internet of things (IoT), intrusion detection system (IDS), incremental learning, pre-processing.

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Section: A Distributed Architecturementioning

confidence: 99%

Section: B Bridge Network: Comparison Phasementioning

confidence: 99%

Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

et al. 2021

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“…Get the latest rules. Literature [27] proposes a new incremental semisupervised learning framework for traffic data. Each layer model includes a generation network, a discriminative structure, and a bridge.…”

Section: Data Mining Technologymentioning

confidence: 99%

Optimization of Teaching Management System Based on Association Rules Algorithm

Qing

2021

Complexity

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The teaching management department carries all the work related to teaching in the whole school. A scientific, efficient, and complete teaching management system cannot only help the teaching management department improve work efficiency and quality but also greatly reduce many problems caused by manual labour risk. This paper designs and implements a teaching management system based on an improved association rule algorithm. First, aiming at the low efficiency of the Apriori algorithm for mining association rules, an association rule model based on interest is proposed. Second, use the MapReduce calculation model to partition the transaction database, then use the improved Apriori optimization algorithm for mining, and finally merge the mining results to obtain frequent itemsets. Through experiments, the optimized algorithm has greatly improved selection mining and computing time than traditional algorithms.

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“…ParsNet monitors the network's variance where complexity reduction procedure is carried out to cope with the high variance situation implying an over-complex network structure -prone to the over-fitting case. V ar can be obtained from (5) and (6)…”

Section: Network Evolution Of Parsnetmentioning

confidence: 99%

“…Although the active learning concept has been shown to reduce the labelling cost significantly [4], these approaches work with the same assumption where the true class label can be immediately obtained regardless of the labelling cost. Another approach using the popular semi-supervised hashing algorithm is proposed and combined with the dynamic feature learning approach of auto-encoder [5,6]. In [7], the closed-loop configuration of the generative and discriminative processes are proposed to deal with partially labelled data streams.…”

Section: Introductionmentioning

confidence: 99%

Weakly Supervised Deep Learning Approach in Streaming Environments

Pratama

Ashfahani

Hady

2019

2019 IEEE International Conference on Big Data (Big Data)

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The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams. A self-evolving deep neural network, namely Parsimonious Network (ParsNet), is proposed as a solution to various weakly-supervised data stream problems. A self-labelling strategy with hedge (SLASH) is proposed in which its auto-correction mechanism copes with the accumulation of mistakes significantly affecting the model's generalization. ParsNet is developed from a closed-loop configuration of the self-evolving generative and discriminative training processes exploiting shared parameters in which its structure flexibly grows and shrinks to overcome the issue of concept drift with/without labels. The numerical evaluation has been performed under two challenging problems, namely sporadic access to ground truth and infinitely delayed access to the ground truth. Our numerical study shows the advantage of ParsNet with a substantial margin from its counterparts in the high-dimensional data streams and infinite delay simulation protocol. To support the reproducible research initiative, the source code of ParsNet along with supplementary materials are made available at https://bit.ly/2qNW7p4.

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Incremental semi-supervised learning on streaming data

Cited by 45 publications

References 22 publications

Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

Optimization of Teaching Management System Based on Association Rules Algorithm

Weakly Supervised Deep Learning Approach in Streaming Environments

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