Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Wicaksono, Ananto Setyo; Supianto, Ahmad Afif

doi:10.14569/ijacsa.2018.091238

Cited by 42 publications

(25 citation statements)

References 17 publications

(26 reference statements)

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“…Here we used a genetic algorithm for stochastic optimization of both MLP and CNN methods. Evolutionary algorithms such as GA are a well-documented alternative to solve complex optimization problems in a faster manner than an exhaustive grid-search procedure (Wicaksono and Supianto, 2018;Han et al, 2020) by selecting, combining, and mutating the model parameters sequentially, thus, mimicking mechanisms that resemble biological evolution.…”

Section: Discussionmentioning

confidence: 99%

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

et al. 2021

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This study focused on assessing the usefulness of using audio signal processing in the gaited horse industry. A total of 196 short-time audio files (4 s) were collected from video recordings of Brazilian gaited horses. These files were converted into waveform signals (196 samples by 80,000 columns) and divided into training (N = 164) and validation (N = 32) datasets. Twelve single-valued audio features were initially extracted to summarize the training data according to the gait patterns (Marcha Batida—MB and Marcha Picada—MP). After preliminary analyses, high-dimensional arrays of the Mel Frequency Cepstral Coefficients (MFCC), Onset Strength (OS), and Tempogram (TEMP) were extracted and used as input information in the classification algorithms. A principal component analysis (PCA) was performed using the 12 single-valued features set and each audio-feature dataset—AFD (MFCC, OS, and TEMP) for prior data visualization. Machine learning (random forest, RF; support vector machine, SVM) and deep learning (multilayer perceptron neural networks, MLP; convolution neural networks, CNN) algorithms were used to classify the gait types. A five-fold cross-validation scheme with 10 repetitions was employed for assessing the models' predictive performance. The classification performance across models and AFD was also validated with independent observations. The models and AFD were compared based on the classification accuracy (ACC), specificity (SPEC), sensitivity (SEN), and area under the curve (AUC). In the logistic regression analysis, five out of the 12 audio features extracted were significant (p < 0.05) between the gait types. ACC averages ranged from 0.806 to 0.932 for MFCC, from 0.758 to 0.948 for OS and, from 0.936 to 0.968 for TEMP. Overall, the TEMP dataset provided the best classification accuracies for all models. The most suitable method for audio-based horse gait pattern classification was CNN. Both cross and independent validation schemes confirmed that high values of ACC, SPEC, SEN, and AUC are expected for yet-to-be-observed labels, except for MFCC-based models, in which clear overfitting was observed. Using audio-generated data for describing gait phenotypes in Brazilian horses is a promising approach, as the two gait patterns were correctly distinguished. The highest classification performance was achieved by combining CNN and the rhythmic-descriptive AFD.

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Section: Discussionmentioning

confidence: 99%

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

et al. 2021

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“…They used GA to obtain the parameters of ML algorithms. As stated in this study, optimum hyperparameters were obtained using GA in a shorter computation time than grid search [21].…”

Section: Hyperparameter Optimization Using Genetic Algorithm (Ga)mentioning

confidence: 99%

Using Long-Short Term Memory Networks with Genetic Algorithm to Predict Engine Condition

Taşabat

Aydın

2022

Gazi University Journal of Science

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Predictive maintenance (PdM) is a type of approach for maintenance processes, allowing maintenance actions to be managed depending on the machine's current condition. Maintenance is therefore carried out before failures occur. The approach doesn't only help avoid abrupt failures but also helps lower maintenance cost and provides possibilities to manufacturers to manage maintenance budgets in a more efficient way. A new deep neural network (DNN) architecture proposed in this study intends to bring a different approach to the predictive maintenance domain.There is an input layer in this architecture, a Long-Short term memory (LSTM) layer, a dropout layer (DO) followed by an LSTM layer, a hidden layer, and an output layer. The number of epochs used in the architecture and the batch size was determined using the Genetic Algorithm (GA). The activation function used after the output layer, DO ratio, and optimization algorithm optimizes loss function determined by using grid search (GS). This approach brings a different perspective to the literature for finding optimum parameters of LSTM. The neural network and hyperparameter optimization approach proposed in this study performs much better than existent studies regarding LSTM network usage for predictive maintenance purposes

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“…This helped the GA process to explore the domain properly and to boost local fine-tuning. Moreover, Wicaksono et al (2018) completely discussed the benefits of GA over GS in comprehensive research on hyperparameter optimization of different ML algorithms. Consequently, this study revealed that GA could be almost as accurate as GS but could significantly decrease the computational costs, particularly when the solution domain is quite large.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the neural network hyperparameters utilizing genetic algorithm

Nikbakht

Anitescu

Rabczuk

2021

J. Zhejiang Univ. Sci. A

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Neural networks (NNs), as one of the most robust and efficient machine learning methods, have been commonly used in solving several problems. However, choosing proper hyperparameters (e.g. the numbers of layers and neurons in each layer) has a significant influence on the accuracy of these methods. Therefore, a considerable number of studies have been carried out to optimize the NN hyperparameters. In this study, the genetic algorithm is applied to NN to find the optimal hyperparameters. Thus, the deep energy method, which contains a deep neural network, is applied first on a Timoshenko beam and a plate with a hole. Subsequently, the numbers of hidden layers, integration points, and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these structures. Thus, applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples.

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Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction

Cited by 42 publications

References 17 publications

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

Using Long-Short Term Memory Networks with Genetic Algorithm to Predict Engine Condition

Optimizing the neural network hyperparameters utilizing genetic algorithm

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