'
다차원 특징 선택을 이용한 다중 모델 기반 조건부 머신러닝 기법' 의 주제별 논문영향력
논문영향력 요약
주제
공학의 다른 갈래
동일주제 총논문수
논문피인용 총횟수
주제별 논문영향력의 평균
33
0
0.0%
주제별 논문영향력
논문영향력
주제
주제별 논문수
주제별 피인용횟수
주제별 논문영향력
주제분류(KDC/DDC)
공학의 다른 갈래
33
0
0.0%
계
33
0
0.0%
* 다른 주제어 보유 논문에서 피인용된 횟수
0
'
다차원 특징 선택을 이용한 다중 모델 기반 조건부 머신러닝 기법' 의 참고문헌
[9] Henderson, T., & Fulcher, B. D., An empirical evaluation of time-series feature sets, In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 1032-1038, IEEE, 2021.
[8] Inan, O., Uzer, M.S., A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation, Arabian Journal for Science and Engineering, vol. 46, pp. 1199–1212, 2021.
[7] Sun, Zhenzhou, et al. An Efficient Noise Elimination Method for Non-stationary and Non-linear Signals by Averaging Decomposed Components, Shock and Vibration, 2022.
[6] El Hajjami, S., Malki, J., Bouju, A., & Berrada, M., Machine learning facing behavioral noise problem in an imbalanced data using one side behavioral noise reduction: application to a fraud detection, International Journal of Computer and Information Engineering, 15(3), pp. 194-205, 2021.
[5] T. Agrawal, Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. New York, NY, USA: Apress, 2021.
[4] Wu, Zhe, et al., Improving Machine Learning Modeling of Nonlinear Processes Under Noisy Data Via Co-teaching Method, In 2021 American Control Conference (ACC), pp. 4660-4666. IEEE, 2021
[34] König, G., Molnar, C., Bischl, B., & Grosse-Wentrup, M., Relative feature importance, In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9318-9325, IEEE, 2021.
[33] Al-Sarem, M., Saeed, F., Boulila, W., Emara, A. H., Al-Mohaimeed, M., & Errais, M., Feature selection and classification using CatBoost method for improving the performance of predicting Parkinsons disease, In Advances on Smart and Soft Computing, Springer, Singapore, pp. 189-199, 2021.
[32] Zhu, X., Chu, J., Wang, K., Wu, S., Yan, W., & Chiam, K., Prediction of rockhead using a hybrid N-XGBoost machine learning framework, Journal of Rock Mechanics and Geotechnical Engineering, vol. 13(6), pp. 1231-1245, 2021.
[31] Charbuty, B., & Abdulazeez, A., Classification based on decision tree algorithm for machine learning, Journal of Applied Science and Technology Trends, vol. 2(1), pp. 20-28, 2021.
[30] Saarela, M., & Jauhiainen, S., Comparison of feature importance measures as explanations for classification models, SN Applied Sciences, vol. 3(2), pp. 1-12, 2021.
[29] Armano, G., & Pegoraro, P. A., Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads, Energies, vol. 15(2), 549, 2022.
[28] Rajbahadur, G. K., Wang, S., Ansaldi, G., Kamei, Y., & Hassan, A. E., The impact of feature importance methods on the interpretation of defect classifiers, IEEE Transactions on Software Engineering, 2021.
[27] Cahuantzi, R., Chen, X., & Güttel, S., A comparison of LSTM and GRU networks for learning symbolic sequences, arXiv preprint arXiv:2107.02248, 2021.
[26] Vennerød, C. B., Kjærran, A., & Bugge, E. S., Long short-term memory RNN, arXiv preprint arXiv:2105.06756, 2021.
[25] Noh, S. H., Analysis of Gradient Vanishing of RNNs and Performance Comparison, Information, vol. 12(11), 442, 2021.
[24] Xiao, C., & Sun, J., Recurrent Neural Networks (RNN), In Introduction to Deep Learning for Healthcare, Springer, Cham, pp. 111-135, 2021.
[23] Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., & Delahoche, L., Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks, Sensors, vol. 22(11), 2022.
[22] Zhang, X., Shen, F., Zhao, J., & Yang, G., Time series forecasting using GRU neural network with multi-lag after decomposition, In International Conference on Neural Information Processing, Springer, Cham, pp. 523-532, 2017.
[2017]
[21] Siami-Namini, S., Tavakoli, N., & Namin, A. S., A comparison of ARIMA and LSTM in forecasting time series, In 2018 17th IEEE international conference on machine learning and applications (ICMLA), pp. 1394-1401, IEEE, 2018.
[2018]
[1] Nielsen, Aileen. Practical time series analysis: Prediction with statistics and machine learning. OReilly Media, 2019.
[2019]
[19] Liu, Y., Gong, C., Yang, L., & Chen, Y., DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction, Expert Systems with Applications, 143, 2022.
[18] Fortuin, V., Baranchuk, D., Rätsch, G., & Mandt, S., Gp-vae: Deep probabilistic time series imputation, In International conference on artificial intelligence and statistics, PMLR, pp. 1651-1661, 2020.
[2020]
[17] Guo, L., Fang, W., Zhao, Q., & Wang, X., The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality, Computers & Industrial Engineering, 161, 107598, 2021.
[16] Gocheva-Ilieva, S., Ivanov, A., & Stoimenova-Minova, M., Prediction of daily mean PM10 concentrations using random forest, CART Ensemble and Bagging Stacked by MARS, Sustainability, vol. 14(2), 798, 2022.
[15] Lin, G., Lin, A., & Cao, J., Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting, Expert Systems with Applications, 168, 114443, 2021.
[14] Wu, M., & Zhang, C., Forecast and Analysis of Seasonal Fluctuation Series based on SARIMA-GRNN Model, Scientific Journal of Economics and Management Research, Vol. 3(8), 2021.
[13] Jiang, Q., Zhu, L., Shu, C. et al., An efficient multilayer RBF neural network and its application to regression problems, Neural Computing & Applications, vol. 34, pp. 4133–4150, 2022.
[12] Guo, Y., Poh, J. W., Wong, C. S. Y. and Ramasamy, S., Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4493-4497, 2022.
[11] Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., & Li, J., Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures, arXiv preprint arXiv:2207.01186, 2022.
[10] Guo, Y., Liu, Z., Ramasamy, S., & Krishnaswamy, P., Uncertainty characterization for predictive analytics with clinical time series data, In Explainable AI in Healthcare and Medicine, Springer, Cham, pp. 69-78, 2021.
LSTM fully convolutional networks for time series classification
