A Survey of Semi-Supervised Learning Methods

Pise, Nitin; Kulkarni, Parag

doi:10.1109/cis.2008.204

Cited by 82 publications

(37 citation statements)

References 4 publications

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“…Thus, unsupervised learning does not require labelled data. On the other hand, semi-supervised learning aims to understand how combining labelled and unlabelled data may change the learning behaviour, and how to design algorithms that take advantage of such a combination [18]. In this research, the focus is on the application of supervised learning techniques to an encrypted VoIP traffic classification, specifically classification of Skype traffic.…”

Section: Introductionmentioning

confidence: 99%

How Robust Can a Machine Learning Approach Be for Classifying Encrypted VoIP?

Alshammari

Zincir-Heywood

2014

J Netw Syst Manage

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The classification of encrypted network traffic represents an important issue for network management and security tasks including quality of service, firewall enforcement, and security. Traffic classification becomes more challenging since the traditional techniques, such as port numbers or Deep Packet Inspection, are ineffective against Peer-to-Peer Voice over Internet Protocol (VoIP) applications, which used non-standard ports and encryption. Moreover, traffic classification also represents a particularly challenging application domain for machine learning (ML). Solutions should ideally be both simple-therefore efficient to deploy-and accurate. Recent advances in ML provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain and increasing the throughput of solutions. In this work, we investigate the robustness of an ML approach to classify encrypted traffic on not only different network traffic but also against evasion attacks. Our ML based approach only employs statistical network traffic flow features without using the Internet Protocol addresses, source/destination ports, and payload information to unveil encrypted VoIP applications in network traffic. What we mean by robust signatures is that the signatures learned by training on one network are still valid when they are applied to traffic coming from totally different locations, networks, time periods, and also against evasion attacks. The results on different network traces, as well as on the evasion of a Skype classifier, demonstrate that the performance of the signatures are very promising, which implies that the statistical information based on the network layer with the use of ML can achieve high classification accuracy and produce robust signatures.

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Section: Introductionmentioning

confidence: 99%

How Robust Can a Machine Learning Approach Be for Classifying Encrypted VoIP?

Alshammari

Zincir-Heywood

2014

J Netw Syst Manage

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“…This term was first introduced for classification problems where there were fewer labeled data (predictors/label pairs) than unlabeled data (predictors without responses). By incorporating unlabeled data into the supervised model or directly training the model from both labeled and unlabeled datasets, SS models are reported to perform better than the classical methods . Traditional SS learning methods include self‐training–based methods, co‐training methods, probabilistic generative model–based methods, and graph‐based methods .…”

Section: Introductionmentioning

confidence: 99%

“…By incorporating unlabeled data into the supervised model or directly training the model from both labeled and unlabeled datasets, SS models are reported to perform better than the classical methods. [8][9][10][11][12] Traditional SS learning methods include self-training-based methods, co-training methods, probabilistic generative model-based methods, and graphbased methods. 11 In the literature, more focus has been put on classification problems rather than regression problems.…”

Section: Introductionmentioning

confidence: 99%

Big data analytics using semi‐supervised learning methods

Frumosu

Külahçı

2018

Quality & Reliability Eng

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The expanding availability of complex data structures requires development of new analysis methods for process understanding and monitoring. In manufacturing, this is primarily due to high‐frequency and high‐dimensional data available through automated data collection schemes and sensors. However, particularly for fast production rate situations, data on the quality characteristics of the process output tend to be scarcer than the available process data. There has been a considerable effort in incorporating latent structure–based methods in the context of complex data. The research question addressed in this paper is to make use of latent structure–based methods in the pursuit of better predictions using all available data including the process data for which there are no corresponding output measurements, ie, unlabeled data. Inspiration for the research question comes from an industrial setting where there is a need for prediction with extremely low tolerances. A semi‐supervised principal component regression method is compared against benchmark latent structure–based methods, principal components regression, and partial least squares, on simulated and experimental data. In the analysis, we show the circumstances in which it becomes more advantageous to use the semi‐supervised principal component regression over these competing methods.

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“…The objective of a clustering algorithm is to partition the given data into mutually exclusive and meaningful [1] clusters; this can provide a better understanding of the natural structure of the data. Semi-supervised [2] classification, which combines strategies from both supervised and unsupervised methods, has also grabbed attention in various fields of research as it requires less human effort and gives better accuracy [3] than unsupervised learning. In this paper, we focus our attention on the challenges faced by clustering algorithms [4,5].…”

Section: Introductionmentioning

confidence: 99%

PFClust: a novel parameter free clustering algorithm

2013

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BackgroundWe present the algorithm PFClust (Parameter Free Clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of ‘correct’ cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings.ResultsWe validate PFClust firstly with reference to a number of synthetic datasets consisting of 2D vectors, showing that its clustering performance is at least equal to that of six other leading methodologies – even though five of the other methods are told in advance how many clusters to use. We also demonstrate the ability of PFClust to classify the three dimensional structures of protein domains, using a set of folds taken from the structural bioinformatics database CATH.ConclusionsWe show that PFClust is able to cluster the test datasets a little better, on average, than any of the other algorithms, and furthermore is able to do this without the need to specify any external parameters. Results on the synthetic datasets demonstrate that PFClust generates meaningful clusters, while our algorithm also shows excellent agreement with the correct assignments for a dataset extracted from the CATH part-manually curated classification of protein domain structures.

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A Survey of Semi-Supervised Learning Methods

Cited by 82 publications

References 4 publications

How Robust Can a Machine Learning Approach Be for Classifying Encrypted VoIP?

How Robust Can a Machine Learning Approach Be for Classifying Encrypted VoIP?

Big data analytics using semi‐supervised learning methods

PFClust: a novel parameter free clustering algorithm

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