Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Continual learning from stationary and non-stationary data

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims

Learning from Data Streams with Randomized Forests

Non-stationary streaming data poses a familiar challenge in machine learning: the need to obtain fast and accurate predictions. A data stream is a continuously generated sequence of data, with data typically arriving rapidly. They are often characterised by a non-stationary generative process, with concept drift occurring as the process changes. Such processes are commonly seen in the real world, such as in advertising, shopping trends, environmental conditions, electricity monitoring and traffic monitoring. Typical stationary algorithms are ill-suited for use with concept drifting data, thus necessitating more targeted methods. Tree-based methods are a popular approach to this problem, traditionally focussing on the use of the Hoeffding bound in order to guarantee performance relative to a stationary scenario. However, there are limited single learners available for regression scenarios, and those that do exist often struggle to choose between similarly discriminative splits, leading to longer training times and worse performance. This limited pool of single learners in turn hampers the performance of ensemble approaches in which they act as base learners. In this thesis we seek to remedy this gap in the literature, developing methods which focus on increasing randomization to both improve predictive performance and reduce the training times of tree-based ensemble methods. In particular, we have chosen to investigate the use of randomization as it is known to be able to improve generalization error in ensembles, and is also expected to lead to fast training times, thus being a natural method of handling the problems typically experienced by single learners. We begin in a regression scenario, introducing the Adaptive Trees for Streaming with Extreme Randomization (ATSER) algorithm; a partially randomized approach based on the concept of Extremely Randomized (extra) trees. The ATSER algorithm incrementally trains trees, using the Hoeffding bound to select the best of a random selection of splits. Simultaneously, the trees also detect and adapt to changes in the data stream. Unlike many traditional streaming algorithms ATSER trees can easily be extended to include nominal features. We find that compared to other contemporary methods ensembles of ATSER trees lead to improved predictive performance whilst also reducing run times. We then demonstrate the Adaptive Categorisation Trees for Streaming with Extreme Randomization (ACTSER) algorithm, an adaption of the ATSER algorithm to the more traditional categorization scenario, again showing improved predictive performance and reduced runtimes. The inclusion of nominal features is particularly novel in this setting since typical categorization approaches struggle to handle them. Finally we examine a completely randomized scenario, where an ensemble of trees is generated prior to having access to the data stream, while also considering multivariate splits in addition to the traditional axis-aligned approach. We find that through the combination of a forgetting mechanism in linear models and dynamic weighting for ensemble members, we are able to avoid explicitly testing for concept drift. This leads to fast ensembles with strong predictive performance, whilst also requiring fewer parameters than other contemporary methods. For each of the proposed methods in this thesis, we demonstrate empirically that they are effective over a variety of different non-stationary data streams, including on multiple types of concept drift. Furthermore, in comparison to other contemporary data streaming algorithms, we find the biggest improvements in performance are on noisy data streams.Engineers Gat

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

Scalable large margin pairwise learning algorithms

2019 Summer.Includes bibliographical references.Classification is a major task in machine learning and data mining applications. Many of these applications involve building a classification model using a large volume of imbalanced data. In such an imbalanced learning scenario, the area under the ROC curve (AUC) has proven to be a reliable performance measure to evaluate a classifier. Therefore, it is desirable to develop scalable learning algorithms that maximize the AUC metric directly. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines. However, the computational cost of the kernelized machines hinders their scalability. To address this problem, we propose a large-scale nonlinear AUC maximization algorithm that learns a batch linear classifier on approximate feature space computed via the k-means Nyström method. The proposed algorithm is shown empirically to achieve comparable AUC classification performance or even better than the kernel AUC machines, while its training time is faster by several orders of magnitude. However, the computational complexity of the linear batch model compromises its scalability when training sizable datasets. Hence, we develop a second-order online AUC maximization algorithms based on a confidence-weighted model. The proposed algorithms exploit the second-order information to improve the convergence rate and implement a fixed-size buffer to address the multivariate nature of the AUC objective function. We also extend our online linear algorithms to consider an approximate feature map constructed using random Fourier features in an online setting. The results show that our proposed algorithms outperform or are at least comparable to the competing online AUC maximization methods. Despite their scalability, we notice that online first and second-order AUC maximization methods are prone to suboptimal convergence. This can be attributed to the limitation of the hypothesis space. A potential improvement can be attained by learning stochastic online variants. However, the vanilla stochastic methods also suffer from slow convergence because of the high variance introduced by the stochastic process. We address the problem of slow convergence by developing a fast convergence stochastic AUC maximization algorithm. The proposed stochastic algorithm is accelerated using a unique combination of scheduled regularization update and scheduled averaging. The experimental results show that the proposed algorithm performs better than the state-of-the-art online and stochastic AUC maximization methods in terms of AUC classification accuracy. Moreover, we develop a proximal variant of our accelerated stochastic AUC maximization algorithm. The proposed method applies the proximal operator to the hinge loss function. Therefore, it evaluates the gradient of the loss function at the approximated weight vector. Experiments on several benchmark datasets show that our proximal algorithm converges to the optimal solution faster than the previous AUC maximization algorithms

A monitoring and threat detection system using stream processing as a virtual function for big data

The late detection of security threats causes a significant increase in the risk of irreparable damages, disabling any defense attempt. As a consequence, fast realtime threat detection is mandatory for security guarantees. In addition, Network Function Virtualization (NFV) provides new opportunities for efficient and low-cost security solutions. We propose a fast and efficient threat detection system based on stream processing and machine learning algorithms. The main contributions of this work are i) a novel monitoring threat detection system based on stream processing; ii) two datasets, first a dataset of synthetic security data containing both legitimate and malicious traffic, and the second, a week of real traffic of a telecommunications operator in Rio de Janeiro, Brazil; iii) a data pre-processing algorithm, a normalizing algorithm and an algorithm for fast feature selection based on the correlation between variables; iv) a virtualized network function in an open-source platform for providing a real-time threat detection service; v) near-optimal placement of sensors through a proposed heuristic for strategically positioning sensors in the network infrastructure, with a minimum number of sensors; and, finally, vi) a greedy algorithm that allocates on demand a sequence of virtual network functions.A detecção tardia de ameaças de segurança causa um significante aumento no risco de danos irreparáveis, impossibilitando qualquer tentativa de defesa. Como consequência, a detecção rápida de ameaças em tempo real é essencial para a administração de segurança. Além disso, A tecnologia de virtualização de funções de rede (Network Function Virtualization - NFV) oferece novas oportunidades para soluções de segurança eficazes e de baixo custo. Propomos um sistema de detecção de ameaças rápido e eficiente, baseado em algoritmos de processamento de fluxo e de aprendizado de máquina. As principais contribuições deste trabalho são: i) um novo sistema de monitoramento e detecção de ameaças baseado no processamento de fluxo; ii) dois conjuntos de dados, o primeiro ´e um conjunto de dados sintético de segurança contendo tráfego suspeito e malicioso, e o segundo corresponde a uma semana de tráfego real de um operador de telecomunicações no Rio de Janeiro, Brasil; iii) um algoritmo de pré-processamento de dados composto por um algoritmo de normalização e um algoritmo para seleção rápida de características com base na correlação entre variáveis; iv) uma função de rede virtualizada em uma plataforma de código aberto para fornecer um serviço de detecção de ameaças em tempo real; v) posicionamento quase perfeito de sensores através de uma heurística proposta para posicionamento estratégico de sensores na infraestrutura de rede, com um número mínimo de sensores; e, finalmente, vi) um algoritmo guloso que aloca sob demanda uma sequencia de funções de rede virtual