| 1. |
- Bayram, Firas, et al.
(författare)
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From concept drift to model degradation : An overview on performance-aware drift detectors
- 2022
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 245
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Forskningsöversikt (refereegranskat)abstract
- The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system’s life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model’s performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model’s performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions.
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| 2. |
- Cacciarelli, Davide, et al.
(författare)
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Stream-based active learning with linear models
- 2022
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Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 254
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Tidskriftsartikel (refereegranskat)abstract
- The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.
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| 3. |
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| 4. |
- Danielson, Mats, et al.
(författare)
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A second-order-based decision tool for evaluating decisions under conditions of severe uncertainty
- 2020
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 191
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Tidskriftsartikel (refereegranskat)abstract
- The requirement to assign precise numerical values to model entities such as criteria weights, probabilities, and utilities is too strong in most real-life decision situations, and hence alternative representations and evaluation mechanisms are important to consider. In this paper, we discuss the DecideIT 3.0 state-of-the-art software decision tool and demonstrate its functionality using a real-life case. The tool is based on a belief mass interpretation of the decision information, where the components are imprecise by means of intervals and qualitative estimates, and we discuss how multiplicative and additive aggregations influence the resulting distribution over the expected values.
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| 5. |
- Deng, Jifei, et al.
(författare)
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Imbalanced multiclass classification with active learning in strip rolling process
- 2022
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Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 255
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Tidskriftsartikel (refereegranskat)abstract
- In the strip rolling process, conventional supervised methods cannot effectively address data with an imbalanced number of normal and faulty instances. In this paper, based on a deep belief network, a resampling method is combined with active learning (AL) to address imbalanced multiclass problems. The support vector machine-based synthetic minority oversampling technique was adapted to enrich the training data, whereas the true data distribution and model generalization were changed. A new selection strategy of AL is proposed that forms a function using uncertainty and diversity. AL uses an optimizing set that has a similar distribution with the whole dataset to calculate the informativeness of instances to optimize the model. Based on this step, the model study instances approach decision boundaries to promote performance. The proposed method is validated by five UCI benchmark datasets and strip rolling data, and experiments show that it outperforms conventional methods in tackling imbalanced multiclass problems.
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| 6. |
- Deshmukh, Shradha, et al.
(författare)
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Explainable quantum clustering method to model medical data
- 2023
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Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 267, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- Medical experts are often skeptical of data-driven models due to the lack of their explainability. Several experimental studies commence with wide-ranging unsupervised learning and precisely with clustering to obtain existing patterns without prior knowledge of newly acquired data. Explainable Artificial Intelligence (XAI) increases the trust between virtual assistance by Machine Learning models and medical experts. Awareness about how data is analyzed and what factors are considered during the decision-making process can be confidently answered with the help of XAI. In this paper, we introduce an improved hybrid classical-quantum clustering (improved qk-means algorithm) approach with the additional explainable method. The proposed model uses learning strategies such as the Local Interpretable Model-agnostic Explanations (LIME) method and improved quantum k-means (qk-means) algorithm to diagnose abnormal activities based on breast cancer images and Knee Magnetic Resonance Imaging (MRI) datasets to generate an explanation of the predictions. Compared with existing algorithms, the clustering accuracy of the generated clusters increases trust in the model-generated explanations. In practice, the experiment uses 600 breast cancer (BC) patient records with seven features and 510 knee MRI records with five features. The result shows that the improved hybrid approach outperforms the classical one in the BC and Knee MRI datasets. © 2023 Elsevier B.V.
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| 7. |
- Galozy, Alexander, 1991-, et al.
(författare)
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Information-gathering in latent bandits
- 2023
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Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 260
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Tidskriftsartikel (refereegranskat)abstract
- In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. © 2022 The Author(s).
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| 8. |
- Guo, Xiaoyi, et al.
(författare)
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Subspace projection-based weighted echo state networks for predicting therapeutic peptides
- 2023
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Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 263
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Tidskriftsartikel (refereegranskat)abstract
- Detection of therapeutic peptide is a major research direction in the current biopharmaceutical field. However, traditional biochemical experimental detection methods take a lot of time. As supplementary methods for biochemical experiments, the computational methods can improve the efficiency of therapeutic peptide detection. Currently, most machine learning-based therapeutic peptide identification algorithms do not consider the processing of noisy samples. We propose a therapeutic peptide classifier, called weighted echo state networks based on subspace projection (WESN-SP), which reduces the bias caused by high-dimensional noisy features and noisy samples. WESN-SP is trained by sparse Bayesian learning algorithm (SBL) and introduces a weight coefficient for each sample by kernel dependence maximization-based subspace projection. The experimental results show that WESN-SP has better performance than other existing methods. © 2023 The Author(s). Published by Elsevier B.V.
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| 9. |
- Haidong, Shao, et al.
(författare)
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Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
- 2020
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Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 188
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Tidskriftsartikel (refereegranskat)abstract
- Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods.
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| 10. |
- Hashim, Fatma A., et al.
(författare)
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Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization
- 2023
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Ingår i: Knowledge-Based Systems. - : ELSEVIER. - 0950-7051 .- 1872-7409. ; 260
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Tidskriftsartikel (refereegranskat)abstract
- Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Ficks law optimization (FLA) is presented, in which Ficks first rule of diffusion is utilized. According to Ficks law of diffusion, molecules tend to diffuse from higher to lower concentration areas. Many experimental series are done to test FLAs performance and ability in solving different optimization problems. Firstly, FLA is tested using twenty well-known benchmark functions and thirty CEC2017 test functions. Secondly, five real-world engineering problems are utilized to demonstrate the feasibility of the proposed FLA. The findings are compared with 12 well-known and powerful optimizers. A Wilcoxon rank-sum test is carried out to evaluate the comparable statistical performance of competing algorithms. Results prove that FLA achieves competitive and promising findings, a good convergence curve rate, and a good balance between exploration and exploitation. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/121033-fick-s-law-algorithm-fla.(c) 2022 Elsevier B.V. All rights reserved.
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