Machine learning approaches for anomaly detection of water quality on a real-world data set*
暂无分享,去创建一个
[1] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[2] Gang Xie,et al. Data-Driven Water Quality Analysis and Prediction: A Survey , 2017, 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService).
[3] Yan Liu,et al. Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.
[4] Jiang Liangzhong,et al. Water Quality Prediction Using LS-SVM and Particle Swarm Optimization , 2009, WKDD.
[5] Chun Kiat Chang,et al. Prediction of water quality index in constructed wetlands using support vector machine , 2015, Environmental Science and Pollution Research.
[6] F L RODKEY. The effect of temperature on the oxidation-reduction potential of the diphosphopyridine nucleotide system. , 1959, The Journal of biological chemistry.
[7] Edward I. Altman,et al. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .
[8] David Byer,et al. Real‐time detection of intentional chemical contamination in the distribution system , 2005 .
[9] Ravi Sankar,et al. Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.
[10] Liangzhong Jiang,et al. Water Quality Prediction Using LS-SVM and Particle Swarm Optimization , 2009 .
[11] Gary King,et al. Logistic Regression in Rare Events Data , 2001, Political Analysis.
[12] Dominique T. Shipmon,et al. Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data , 2017, ArXiv.
[13] Trevor Hastie,et al. An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.
[14] Andrew W. Senior,et al. Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.
[15] Doina Logofatu,et al. Approaches to Building a Detection Model for Water Quality: A Case Study , 2018, ACIIDS.
[16] R. A. Bottenberg,et al. APPLIED MULTIPLE LINEAR REGRESSION , 1964 .
[17] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[18] Doina Logofatu,et al. Applying Tree Ensemble to Detect Anomalies in Real-World Water Composition Dataset , 2018, IDEAL.
[19] Guy Lapalme,et al. A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..
[20] Rodkey Fl,et al. The effect of temperature on the oxidation-reduction potential of the diphosphopyridine nucleotide system. , 1959 .
[21] Mohamed Bekkar,et al. Evaluation Measures for Models Assessment over Imbalanced Data Sets , 2013 .
[22] Doina Logofatu,et al. Review on General Techniques and Packages for Data Imputation in R on a Real World Dataset , 2018, ICCCI.
[23] Simon Fong,et al. An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets , 2013, DaEng.
[24] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[25] Prudence W. H. Wong,et al. A real-time anomaly detection algorithm/or water quality data using dual time-moving windows , 2017, 2017 Seventh International Conference on Innovative Computing Technology (INTECH).
[26] Francisco Herrera,et al. Data Preprocessing in Data Mining , 2014, Intelligent Systems Reference Library.
[27] Mi Zhang,et al. A feature selection-based framework for human activity recognition using wearable multimodal sensors , 2011, BODYNETS.