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Amelioration of multitudinous classifiers performance with hyper-parameters tuning in elephant search optimization for cardiac arrhythmias detection

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JOURNAL OF SUPERCOMPUTING 2024年第10期80卷 14848-14924页

作者： Manivannan, Gowri Shankar Babu, C. Ganesh Rajaguru, Harikumar Bannari Amman Inst Technol Sathyamangalam 638401 Tamilnadu India

Detecting cardiac abnormalities promptly is critical for preventing unexpected and premature fatalities. In this research, four types of cardiac arrhythmias such as Ventricular Tachycardia, Premature Ventricular Contraction, Normal Sinus Rhythm and Supraventricular Tachycardia are detected from the amassed Physiobank MIT-BIH cardiac arrhythmia database. Dimensionality reduction techniques like Stochastic Neighbour Embedding (SNE), Neighbourhood Preserving Embedding, Linear Local Tangent Space Alignment and Gaussian Process Latent Variable Model are used to reduce the dimension of the ECG signals. The appropriate features of dimensionally reduced ECG signals are selected by the Elephant Search Optimization (ESO) technique. Finally, classification is performed using the relevant classifiers, such as Support Vector Machine, Adaboost, Modest Adaboost based on Ridge Regression (Modest ***), Extreme Gradient Boost (XGboost) and Naive Bayes (NBC) classifiers. Multiple classifiers without and with ESO feature selection for different cardiac cases have an average classification accuracy of 62.23% and 73.61%, respectively. These multiple classifiers are defined by a set of control parameters known as hyper-parameters, which must be tuned in order to achieve optimal results. Experts have developed many approaches for detecting cardiac arrhythmias, but these multiple classifiers do not always perform well when the usual parameters for machine learning classification models are employed. In this paper, various classifiers are used in conjunction with the Stochastic Gradient Descent (SGD), Particle Swarm Optimization (PSO) and Bayesian Tree-structured Parzen Estimator (BTPE) to enhance the cardiac arrhythmia classification accuracy via hyper-parameter tuning. Multiple classifiers with SGD, PSO and BTPE hyper-parameters tuning techniques for various cardiac cases have an average classification accuracy of 80.13%, 90.67% and 94.96%, respectively. The Classifier's performanc

关键词： Arrhythmias BTPE Cardiovascular diseases hyper-parameters PSO SGD

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BAYESIAN OPTIMIZATION OF hyper-parameters AND REWARD FUNCTION IN DEEP REINFORCEMENT LEARNING: APPLICATION TO BEHAVIOR LEARNING OF MOBILE ROBOT

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2025年第2期21卷 469-480页

作者： Nishimura, Takuto Sota, Ryosuke Horiuchi, Tadashi Matsue Coll Natl Inst Technol Adv Engn Fac 14-4 Nishi Ikuma Matsue Shimane 6908518 Japan Nara Inst Sci & Technol Grad Sch Sci & Technol 8916-5 Takayama Cho Ikoma Nara 6300192 Japan

. Deep reinforcement learning is a machine learning method that combines deep learning and reinforcement learning. Deep Q-Network (DQN) is one of the typical methods of deep reinforcement learning. DQN uses Convolutional Neural Network (CNN) which can extract features from the input images. We have applied DQN method to the mobile robot navigation problem. The values of hyper-parameters, including the network structure of DQN, and the reward function used in the DQN algorithm, have been determined empirically. In this study, we attempt to optimize the values of hyper-parameters and reward function of deep reinforcement learning by using Bayesian optimization. We realized to optimize the values of hyper-parameters including the network structure of DQN, and the reward function by Optuna, a framework of Bayesian optimization. We confirmed that our method using the values of hyper-parameters and reward function obtained by Optuna have more goal achievements and fewer action steps to achieve the goal among test runs after learning than those by empirical method.

关键词： Deep reinforcement learning Bayesian optimization hyper-parameters Reward function Mobile robot Behavior acquisition

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A lightweight knowledge-based PSO for SVM hyper-parameters tuning in a dynamic environment

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JOURNAL OF SUPERCOMPUTING 2023年第16期79卷 18777-18799页

作者： Kalita, Dhruba Jyoti Singh, Vibhav Prakash Kumar, Vinay Natl Inst Technol Dept Comp Sci & Engn Jamshedpur India Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Prayagraj India

hyper-parameter optimization is a crucial task for designing kernel-based machine learning models. Their values can be set by using various optimization algorithms. But a data-dependent objective function makes hyper-parameter's configuration changes over time in a dynamic environment. A dynamic environment is an environment where training data keep getting added continuously over time. To find the optimum values for the hyper-parameters in such an environment, one needs to run the optimization algorithm repeatedly over time. But due to the dependency of the objective function on data, the average time complexity of the optimization process increases. This paper work proposes a novel knowledge-based approach that uses particle swarm optimization (PSO) as the base optimization algorithm to optimize the hyper-parameters of support vector machine. We have introduced two major modules for designing this framework-a knowledge transfer module and a drift detection module. The knowledge transfer module in our proposed framework generates knowledge by running PSO and transfers this knowledge to the consequent time instances. On the other hand, the drift detection module is responsible for detecting changes in the objective function when new data get added to the existing data. This drift detection module helps to utilize the transferred knowledge at a particular time instance to reduce the execution time of the overall optimization process. The proposed framework has been evaluated using various standard datasets such as Adult, DNA, Nist-Digits, Segment, Splice, Mushroom and Usps for five consecutive time instances and found the average execution time as 30.72 s, which is better than the execution time 36.89 s recorded using general PSO. We have also found that our proposed framework performs the optimization of hyper-parameters much faster than the other existing approaches such as grid search, chained-PSO, dynamic model selection and quantized dynamic multi-PSO.

关键词： Support vector machine hyper-parameters Swarm intelligence Particle swarm optimization

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Constraints on hyper-parameters in Deep Learning Convolutional Neural Networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第11期13卷 439-449页

作者： Al-Saggaf, Ubaid M. Botalb, Abdelaziz Faisal, Muhammad Moinuddin, Muhammad Alsaggaf, Abdulrahman U. Alfakeh, Sulhi Ali King Abdulaziz Univ Electricaland Comp Engn Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah 21589 Saudi Arabia King Fahd Univ Petr & Minerals Comp & Informat Technol Dept Dammam Commun Coll Dhahran 31261 Saudi Arabia King Abdulaziz Univ Child & Adolescent Psychiatrist Fac Med Dept Internal Med Jeddah 21589 Saudi Arabia

Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding. The reason why the task of tuning parameters optimization is difficult in the CNN is the existence of a huge optimization space comprising a large number of hyper-parameters such as the number of layers, number of neurons, number of kernels, stride, padding, rows or columns truncation, parameters of the backpropagation algorithm, etc. Moreover, most of the existing techniques in the literature for the selection of these parameters are based on random practice which is developed for some specific datasets. In this work, we empirically investigated and proved that CNN performance is linked not only to choosing the right hyper-parameters but also to its implementation. More specifically, it is found that the performance is also depending on how it deals when the CNN operations require setting of hyper-parameters that do not symmetrically fit the input volume. We demonstrated two different implementations, crop or pad the input volume to make it fit. Our analysis shows that padding performs better than cropping in terms of prediction accuracy (85.58% in contrast to 82.62%) while takes lesser training time (8 minutes lesser).

关键词： Neural networks convolution pooling hyper-parameters CNN deep learning zero-padding stride back-propagation

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A novel adaptive optimization framework for SVM hyper-parameters tuning in non-stationary environment: A case study on intrusion detection system

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Kalita, Dhruba Jyoti Singh, Vibhav Prakash Kumar, Vinay Dept Comp Sci & Engn Mumbai India Natl Inst Technol Jamshedpur 831014 India Motilal Nehru Natl Inst Technol Allahabad Prayagraj 211004 India

Building an Intrusion Detection System (IDS) in non-stationary environment is challenging because, in such an environment, intrusion-related data grow every day. A machine learning model trained in a stationary envi-ronment where training data does not change, often fails to retain its performance in real world environment. This is because dynamism in data makes the hyper-parametric values of the underlying classifier shift in the search space. For making such a model work for intrusion detection in non-stationary environment, one must have to run hyper-parametric optimization algorithm again and again at various time instances. But the expansion of the existing data in non-stationary environment, makes such a way of tunning the hyper-parameters computationally expensive. So, there is a requirement of more adaptive and computationally efficient optimi-zation frameworks for hyper-parameters to build IDS in non-stationary environment. This paperwork proposes a novel framework to train a Support Vector Machine (SVM) for intrusion detection by optimizing its hyper -parameters C and gamma dynamically. For designing this framework, we have used Moth-Flame Optimization (MFO) as the base optimization algorithm which can be run with random initialization. Further, for utilizing the knowledge generated by running the base optimization algorithm, we have introduced two algorithms-a Lightweight MFO and a simple Knowledge-Based Search. The Lightweight MFO uses the knowledge for initial-izing the starting solutions and the Knowledge-Based Search uses the knowledge as search space. Based on the result of a drift detection module, the proposed framework identifies the most appropriate algorithm to be used at a particular time instance when re-training of the model is required due to the change in the data. Results have shown a significant reduction in the average time complexity of the hyper-parametric optimization process. We have evaluated our proposed framework on benchmark NSL-

关键词： Intrusion Detection System Support Vector Machine Moth-Flame Optimization hyper-parameters Meta-heuristics

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Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年 166卷 241-254页

作者： Pan, Suoyan Guan, Haiyan Chen, Yating Yu, Yongtao Goncalves, Wesley Nunes Marcato Junior, Jose Li, Jonathan Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing 210044 JS Peoples R China Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 JS Peoples R China Suzhou Xiaoqi Informat Technol Co Ltd 162 Renmin South Rd Taicang 215400 Jiangsu Peoples R China Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Univ Fed Mato Grosso do Sul Fac Comp Sci Campo Grande MS Brazil Univ Fed Mato Grosso do Sul Fac Engn Architecture & Urbanism & Geog Campo Grande MS Brazil Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

Multispectral LiDAR (Light Detection And Ranging) is characterized of the completeness and consistency of its spectrum and spatial geometric data, which provides a new data source for land-cover classification. In recent years, the convolutional neural network (CNN), compared with traditional machine learning methods, has made a series of breakthroughs in image classification, object detection, and image semantic segmentation due to its stronger feature learning and feature expression abilities. However, traditional CNN models suffer from some issues, such as a large number of layers, leading to higher computational cost. To address this problem, we propose a CNN-based multi-spectral LiDAR land-cover classification framework and analyze its optimal parameters to improve classification accuracy. This framework starts with the preprocessing of multi-spectral 3D LiDAR data into 2D images. Next, a CNN model is constructed with seven fundamental functional layers, and its hyper-parameters are comprehensively discussed and optimized. The constructed CNN model with the optimized hyper-parameters was tested on the Titan multi-spectral LiDAR data, which include three wavelengths of 532 nm, 1064 nm, and 1550 nm. Extensive experiments demonstrated that the constructed CNN with the optimized hyper-parameters is feasible for multi-spectral LiDAR land-cover classification tasks. Compared with the classical CNN models (i.e., AlexNet, VGG16 and ResNet50) and our previous studies, our constructed CNN model with the optimized hyper-parameters is superior in computational performance and classification accuracies.

关键词： Multi-spectral LiDAR CNN Land-cover classification hyper-parameters

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SYSTEMATIC ANALYSIS AND AUTOMATED SEARCH OF hyper-parameters FOR CELL CLASSIFIER TRAINING 17

SYSTEMATIC ANALYSIS AND AUTOMATED SEARCH OF HYPER-PARAMETERS...

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17th IEEE International Symposium on Biomedical Imaging Workshops (IEEE ISBI)

作者： Graebel, Philipp Nickel, Gregor Crysandt, Martina Herwartz, Reinhild Baumann, Melanie Klinkhammer, Barbara M. Boor, Peter Bruemmendorf, Tim H. Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Dept Hematol & Oncol Aachen Germany Rhein Westfal TH Aachen Univ Hosp Inst Pathol Aachen Germany

ISBN: (纸本)9781728174013

Performance and robustness of neural networks depend on a suitable choice of hyper-parameters, which is important in research as well as for the final deployment of deep learning algorithms. While a manual systematical analysis can be too time consuming, a fully automatic search is very dependent on the kind of hyper-parameters. For a cell classification network, we assess the individual effects of a large number of hyper-parameters and compare the resulting choice of hyper-parameters with state of the art search techniques. We further propose an approach for automated, successive search space reduction that yields well performing sets of hyper-parameters in a time-efficient way.

关键词： Cell Classification hyper-parameters hyper-parameter Optimization

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COV Based Classification of PQ Disturbances Using Wavelet and Optimized SVM hyper-parameters

COV Based Classification of PQ Disturbances Using Wavelet an...

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International Conference on Computational Performance Evaluation (ComPE)

作者： Rout, Pritam Chauhan, Abhishek Singh, Ksh Milan NIT Meghalaya Elect Engn Shillong Meghalaya India

ISBN: (纸本)9781728166445

This paper presents study of a feature, Coefficient of Variation (COV) based on wavelet transform (WT) for classification of power quality (PQ) disturbances. A five level wavelet decomposition using Db4 wavelet is chosen for all feature extraction processes. Six types of power quality disturbances (PQDs) have been considered. The feasibility of COV has been evaluated along with four other features using linear and Gaussian kernel support vector machine (SVM). Classification accuracy has been improved by optimizing the hyper-parameters for a Gaussian kernel SVM through Bayesian optimization (BO). BO-SVM provides better accuracy than a linear kernel SVM. From the experimental analysis it is revealed that the proposed method can effectively detect and identify PQDs.

关键词： Wavelet Transform Coefficient of Variation SVM Bayesian Optimization hyper-parameters

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Selecting hyper-parameters of Gaussian Process Regression Based on Non-Inertial Particle Swarm Optimization in Internet of Things

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IEEE ACCESS 2019年 7卷 59504-59513页

作者： Kang, Lanlan Chen, Ruey-Shun Xiong, Naixue Chen, Yeh-Cheng Hu, Yu-Xi Chen, Chien-Ming Jiangxi Univ Sci & Technol Coll Appl Sci Gangzhou 341000 Peoples R China Dongguan Polytech Dept Comp Engn Dongguan 523808 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Northwestern Polytech Univ Sch Software & Microelect Xian 710129 Shaanxi Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China

Gaussian process regression (GPR) is frequently used for uncertain measurement and prediction of nonstationary time series in the Internet of Things data, nevertheless, the generalization and regression efficacy of GPR are directly impacted by its selection of hyper-parameters. In the study, a non-inertial particle swarm optimization with elite mutation-Gaussian process regression (NIPSO-GPR) is proposed to optimize the hyper-parameters of GPR. NIPSO-GPR can adaptively obtain hyper-parameters of GPR via uniform non-inertial velocity update formula and adaptive elite mutation strategy. When compared with several frequently used algorithms of hyper-parameters optimization on linear and nonlinear time series sample data, experimental results indicate that GPR after hyper-parameters optimized by NIPSO-GPR has better fitting precision and generalization ability.

关键词： Mutation Gaussian process regression time series regression hyper-parameters non-inertial particle swarm optimization

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A dynamic framework for tuning SVM hyper parameters based on Moth-Flame Optimization and knowledge-based-search

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114139-114139页

作者： Kalita, Dhruba Jyoti Singh, Vibhav Prakash Kumar, Vinay Natl Inst Technol Dept Comp Sci & Engn Jamshedpur 831014 Bihar India Motilal Nehru Natl Inst Technol Dept Comp Sci & Engn Prayagraj 211004 India

In the real world, most of the collections of data are dynamic in nature, i.e. their size may grow with time. This dynamic nature of the data not only reduces the performance of the classifiers but also demands more optimized models for retaining the performance. Due to this, machine learning models developed in a static environment cannot be deployed efficiently to solve the real-world problems. Nowadays, maximum existing works consider only the static behaviour of the data for the training of machine learning models where the size of the collection of training data does not change over time. This paperwork imposes Support Vector Machine (SVM) in a dynamic environment. It has been identified that shifting of the optimum values of two hyper-parameters C (Penalty Parameter) and gamma (Kernel Parameter) in the search space is one of the primary reasons for the performance degradation of SVM in dynamic environment. This paper proposes a novel framework that uses a new optimization module Knowledge-Based-Search (KBS) along with Moth -Flame Optimization (MFO) to optimize C and gamma in a dynamic environment to train SVM efficiently. KBS uses knowledge gathered at various instances of time, which are the bi-products of MFO. MFO in our framework is the base optimization algorithm which works underneath KBS. The experiments have shown that KBS helps in controlling the exponential growth of the time complexity of the optimization process where only MFO is used to optimize C and gamma. Integration of KBS with MFO brings down the time complexity to a large extent. To validate the proposed framework we have used a simulated dynamic environment for profit/loss classification problem for organizations. The experiments have also shown that KBS's integration with MFO outperforms integration of KBS with other modern optimization techniques such as Particle Swarm Optimization (PSO), Multi-Verse Optimization (MVO), Grey-Wolf Optimization (GWO), Cuckoo Search (CS), Whale Optimization A

关键词： Support Vector Machine hyper-parameters Model selection Swarm Intelligence Moth-Flame Optimization

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