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Optimised one-class classification performance

MACHINE LEARNING 2022年第8期111卷 2863-2883页

作者： Lenz, Oliver Urs Peralta, Daniel Cornelis, Chris Univ Ghent Dept Appl Math Comp Sci & Stat Ghent Belgium Univ Ghent Dept Informat Technol IDLab IMEC Ghent Belgium

We provide a thorough treatment of one-class classification with hyperparameter optimisation for five data descriptors: Support Vector Machine (SVM), Nearest Neighbour Distance (NND), Localised Nearest Neighbour Distance (LNND), Local Outlier Factor (LOF) and Average Localised Proximity (ALP). The hyperparameters of SVM and LOF have to be optimised through cross-validation, while NND, LNND and ALP allow an efficient form of leave-one-out validation and the reuse of a single nearest-neighbour query. We experimentally evaluate the effect of hyperparameter optimisation with 246 classification problems drawn from 50 datasets. From a selection of optimisation algorithms, the recent Malherbe-Powell proposal optimises the hyperparameters of all data descriptors most efficiently. We calculate the increase in test AUROC and the amount of overfitting as a function of the number of hyperparameter evaluations. After 50 evaluations, ALP and SVM significantly outperform LOF, NND and LNND, and LOF and NND outperform LNND. The performance of ALP and SVM is comparable, but ALP can be optimised more efficiently so constitutes a good default choice. Alternatively, using validation AUROC as a selection criterion between ALP or SVM gives the best overall result, and NND is the least computationally demanding option. We thus end up with a clear trade-off between three choices, allowing practitioners to make an informed decision.

关键词： Data descriptors hyperparameter optimisation Novelty detection One-class classification Semi-supervised outlier detection

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A Gaussian-Process-Based Data-Driven Traffic Flow Model and Its Application in Road Capacity Analysis

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第2期24卷 1544-1563页

作者： Liu, Zhiyuan Lyu, Cheng Wang, Zelin Wang, Shuaian Liu, Pan Meng, Qiang Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hong Kong Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 119077 Singapore

To estimate the accurate fundamental relationship in traffic flow, this paper proposes a novel framework that extends classical fundamental diagram (FD) models to incorporate more dimensions of traffic state variables and allow for the impact of the supply-side factors of roads. The proposed framework is suitable for real-time traffic management, especially in urban areas, due to its reliance on minimal assumptions, its flexibility in adapting to various data sources, and its scalability to higher-dimensional data. The Gaussian process (GP) model is adopted as the base model for learning the optimal mapping from these input features to traffic volume. To enhance the GP model, an in-depth analysis of the properties of its kernel and likelihood function is provided. To cope with the hyperparameter optimisation of the GP, a modified Newton method for GP-based traffic flow model is also designed, which can jump over regions with small gradients. Experiments based on simulation data demonstrate the ability of the proposed framework to capture complex relationships between traffic state variables and supply-side factors, and show its value for estimating dynamic road capacity.

关键词： Roads Data models Mathematical models Analytical models Solid modeling Sensors Predictive models Traffic flow fundamental diagram Gaussian process road capacity hyperparameter optimisation

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BACS: blockchain and AutoML-based technology for efficient credit scoring classification

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ANNALS OF OPERATIONS RESEARCH 2022年第2期345卷 703-723页

作者： Yang, Fan Qiao, Yanan Qi, Yong Bo, Junge Wang, Xiao Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China

Credit evaluation is of high scientific significance and practical use, especially in today's plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.

关键词： Credit scoring Credit crisis hyperparameter optimisation Blockchain technology Classification model Automated machine learning

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Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree-structured parzen estimators

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2022年第3期4卷 194-207页

作者： Liang, Jingkang Liao, Yixiao Chen, Zhuyun Lin, Huibin Jin, Gang Gryllias, Konstantinos Li, Weihua South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China Beijing Informat Sci Technol Univ Beijing Key Lab Measurement Control Mech & Elect Beijing Peoples R China Guangdong Prov Key Lab Tech & Equipment Macromol Guangzhou Peoples R China Katholieke Univ Leuven Dept Mech Engn Leuven Belgium Flanders Make Dynam Mech & Mechatron Syst Lommel Belgium South China Univ Technol Shine Ming Wu Sch Intelligent Engn Guangzhou Peoples R China

Deep learning-based methods have been widely used in the field of rotating machinery fault diagnosis. It is of practical significance to improve the calculation speed of the model on the premise of ensuring accuracy, so as to realise real-time fault diagnosis. However, designing an efficient and lightweight fault diagnosis network requires expert knowledge to determine the network structure and adjust the hyperparameters of the network, which is time-consuming and laborious. In order to design fault diagnosis networks considering both time and accuracy effortlessly, a novel lightweight network with modified tree-structured parzen estimators (LN-MT) is proposed for intelligent fault diagnosis of rotating machinery. Firstly, a lightweight framework based on global average pooling and group convolution is proposed, and a hyperparameter optimisation (HPO) method based on Bayesian optimisation called tree-structured parzen estimator is utilised to automatically search the optimal hyperparameters for the fault diagnosis task. The objective of the HPO algorithm is the weighting of accuracy and calculating time, so as to find models that balance both time and accuracy. The results of comparison experiments indicate that LN-MT can achieve superior fault diagnosis accuracies with few trainable parameters and less calculating time.

关键词： convolutional neural network fault diagnosis hyperparameter optimisation neural architecture search

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Towards efficient similarity embedded temporal Transformers via extended timeframe analysis

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COMPLEX & INTELLIGENT SYSTEMS 2024年第4期10卷 4793-4815页

作者： Olorunnimbe, Kenniy Viktor, Herna Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

Price prediction remains a crucial aspect of financial market research as it forms the basis for various trading strategies and portfolio management techniques. However, traditional models such as ARIMA are not effective for multi-horizon forecasting, and current deep learning approaches do not take into account the conditional heteroscedasticity of financial market time series. In this work, we introduce the similarity embedded temporal Transformer (SeTT) algorithms, which extend the state-of-the-art temporal Transformer architecture. These algorithms utilise historical trends in financial time series, as well as statistical principles, to enhance forecasting performance. We conducted a thorough analysis of various hyperparameters including learning rate, local window size, and the choice of similarity function in this extension of the study in a bid to get optimal model performance. We also experimented over an extended timeframe, which allowed us to more accurately assess the performance of the models in different market conditions and across different lengths of time. Overall, our results show that SeTT provides improved performance for financial market prediction, as it outperforms both classical financial models and state-of-the-art deep learning methods, across volatile and non-volatile extrapolation periods, with varying effects of historical volatility on the extrapolation. Despite the availability of a substantial amount of data spanning up to 13 years, optimal results were primarily attained through a historical window of 1-3 years for the extrapolation period under examination.

关键词： Deep learning Financial price prediction Temporal Transformer Stock market forecast Multi-horizon forecast hyperparameter optimisation

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Multi-fault Diagnosis of Rotating Machine Under Uncertain Speed Conditions

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第3期12卷 4637-4654页

作者： Mishra, R. K. Choudhary, Anurag Fatima, S. Mohanty, A. R. Panigrahi, B. K. Indian Inst Technol Delhi Ctr Automot Res & Tribol New Delhi India Indian Inst Technol Delhi Sch Interdisciplinary Res SIRe New Delhi India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur W Bengal India Indian Inst Technol Delhi Dept Elect Engn New Delhi India

BackgroundMulti-faults in rotating machines are critical and create an unfavourable working environment. Research on multi-faults is still in the early stages, and models are trained at constant speeds, which are not efficient under different types of uncertain speed *** paper proposes developing a multi-fault diagnosis system that can work irrespective of speed conditions. Vibration signatures were acquired from the different experimental multi-fault conditions under an uncertain operating environment. STFNet (Segmented Time-frequency Network) was applied to extract fault information and to create an intelligent multi-fault classification *** and validationRigorous testing of the proposed STFNet model was done under various uncertain speed conditions like constant, continuously accelerated, continuously decelerated and fluctuating speed, and a maximum accuracy of 97.87 % was achieved. The performance of the proposed model was compared with the existing algorithms and was validated on another bearing multi-fault *** per the industrial requirement, the proposed approach will be very helpful for diagnosing multi-faults under various uncertain speed conditions.

关键词： Multi-fault Motor Bearing Speed variation hyperparameter optimisation STFNet

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A comparative analysis of deep neural network architectures for sentence classification using genetic algorithm

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EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1933-1952页

作者： Rogers, Brendan Noman, Nasimul Chalup, Stephan Moscato, Pablo Univ Newcastle Sch Informat & Phys Sci Univ Dr Callaghan NSW 2308 Australia

Because of the number of different architectures, numerous settings of their hyper-parameters and disparity among their sizes, it is difficult to equitably compare various deep neural network (DNN) architectures for sentence classification. Evolutionary algorithms are emerging as a popular method for the automatic selection of architectures and hyperparameters for DNNs whose generalisation performance is heavily impacted by such settings. Most of the work in this area is done in the image domain, leaving text analysis, another prominent application domain of deep learning, largely absent. Besides, literature presents conflicting claims regarding the superiority of one DNN architecture over others in the context of sentence classification. To address this issue, we propose a genetic algorithm (GA) for optimising the architectural and hyperparameter settings in different DNN types for sentence classification. To enable the representation of the wide variety of architectures and hyperparameters utilised in DNNs, we employed a generalised and flexible encoding scheme in our GA. Our study involves optimising two convolutional and three recurrent architectures to ensure a fair and unbiased evaluation of their performance. Furthermore, we explore the effects of using F1 score versus accuracy as a performance metric during evolutionary optimisation of those architectures. Our results, using ten datasets, show that, in general, the architectures and hyperparameters evolved using the F1 score tended to outperform those evolved using accuracy and in the case of CNN and BiLSTM the results were significant in statistical measures. Of the five architectures considered, the GA-evolved gated recurrent unit (GRU) performed the strongest overall, achieving good generalisation performance while using relatively few trainable parameters, establishing GRU as the preferred architecture for the sentence classification task. The optimised architectures exhibited comparable performance with

关键词： Sentence classification hyperparameter optimisation Deep neural network Genetic algorithm

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Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

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PEERJ COMPUTER SCIENCE 2021年 7卷 e444-e444页

作者： Kalliola, Jussi Kapociute-Dzikiene, Jurgita Damasevicius, Robertas Vytautas Magnus Univ Dept Appl Informat Kaunas Lithuania Silesian Tech Univ Fac Appl Math Gliwice Poland

Accurate price evaluation of real estate is beneficial for many parties involved in real estate business such as real estate companies, property owners, investors, banks, and financial institutes. Artificial Neural Networks (ANNs) have shown promising results in real estate price evaluation. However, the performance of ANNs greatly depends upon the settings of their hyperparameters. In this paper, we apply and optimize an ANN model for real estate price prediction in Helsinki, Finland. Optimization of the model is performed by fine-tuning hyper-parameters (such as activation functions, optimization algorithms, etc.) of the ANN architecture for higher accuracy using the Bayesian optimization algorithm. The results are evaluated using a variety of metrics (RMSE, MAE, R2) as well as illustrated graphically. The empirical analysis of the results shows that model optimization improved the performance on all metrics (reaching the relative mean error of 8.3%).

关键词： Artificial neural network hyperparameter optimisation Prediction model Real estate prices

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SMBOX: A Scalable and Efficient Method for Sequential Model-Based Parameter Optimization 9th

SMBOX: A Scalable and Efficient Method for Sequential Model-...

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9th Annual Conference on Machine Learning, Optimization and Data science (LOD)

作者： Salhi, Tarek Woodward, John Loughborough Univ Loughborough Leics England

ISBN: (纸本)9783031539657;9783031539664

The application of Machine Learning (ML) algorithms continues to grow and shows no signs of slowing down. Each ML algorithm has an associated set of hyperparameter values that need to be set to achieve the best performance for each problem. The task of selecting the best parameter values for the problem at hand is known as hyperparameter optimisation (HPO). Traditionally this has been carried out manually or by unguided programmatic approaches such as grid or random search. These approaches can be extremely time-consuming and inefficient, especially when dealing with more than a handful of parameters. More advanced methods involving Evolutionary Heuristics [23] or Bayesian optimisation [17,28] use a guided search approach and are widely considered as the gold standard approach for hyperparameter optimisation. In this paper, we introduce SMBOX (https://***/smbox/ smbox), a novel HPO search strategy developed to rival the state-ofthe-art, SMAC [15]. Our benchmarking on public classification datasets, against both SMAC and a Random search baseline, shows that SMBOX not only challenges SMAC in tuning hyperparameters for two prevalent ML algorithms, but it also excels in finding good hyperparameter values quicker than SMAC. This rapid optimisation capability is extremely powerful, particularly in situations where time or computational resources are constrained or costly.

关键词： hyperparameter optimisation Machine Learning Classification

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Employing Bottleneck and Convolutional Features for Speech-Based Physical Load Detection on Limited Data Amounts 20

Employing Bottleneck and Convolutional Features for Speech-B...

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Interspeech Conference

作者： Egorow, Olga Mrech, Tarik Weisskirchen, Norman Wendemuth, Andreas Otto von Guericke Univ Cognit Syst Grp Magdeburg Germany Fraunhofer Inst Factory Operat & Automat Virtual Engn Magdeburg Germany

The detection of different levels of physical load from speech has many applications: Besides telemedicine, non-contact detection of certain heart rate ranges can be useful for sports and other leisure time devices. Available approaches mainly use a high number of spectral and prosodic features. In this setting of typically small data sets, such as the Talk & Run data set and the Munich Biovoice Corpus, the high-dimensional feature spaces are only sparsely populated. Therefore, we aim at a reduction of the feature number using modern neural net inspired features: Bottleneck layer features, obtained from standard low-level descriptors via a feed-forward neural network, and activation map features, obtained from spectrograms via a convolutional neural network. We use these features for an SVM classification of high and low physical load and compare their performance. We also discuss the possibility of hyperparameter transfer of the extracting networks between different data sets. We show that even for limited amounts of data, deep learning based methods can bring a substantial improvement over "conventional" features.

关键词： deep learning bottleneck features convolutional neural networks hyperparameter optimisation physical stress detection physical load detection speech signal processing

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