A framework for learning and embedding multi-sensor forecasting models into a decision support system: A case study of methane concentration in coal mines

[1]  Dominik Slezak,et al.  On the role of feature space granulation in feature selection processes , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[2]  Chengwu Li,et al.  Automatic crack detection method for loaded coal in vibration failure process , 2017, PloS one.

[3]  Hamido Fujita,et al.  An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view , 2017, Inf. Sci..

[4]  Marcin Michalak,et al.  Predicting seismic events in coal mines based on underground sensor measurements , 2017, Eng. Appl. Artif. Intell..

[5]  Kan Li,et al.  Fuzzy competence model drift detection for data-driven decision support systems , 2017, Knowl. Based Syst..

[6]  Rossitza Goleva,et al.  Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering , 2017, IEEE Access.

[7]  Damien Fay,et al.  Multiple adaptive mechanisms for data-driven soft sensors , 2017, Comput. Chem. Eng..

[8]  Uwe Mönks,et al.  Information Fusion of Conflicting Input Data , 2016, Sensors.

[9]  Marek Sikora,et al.  Modern ICT and Mechatronic Systems in Contemporary Mining Industry , 2016, IJCRS.

[10]  Marek Grzegorowski,et al.  Massively parallel feature extraction framework application in predicting dangerous seismic events , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[11]  David de la Fuente,et al.  Intelligent Decision Support System for Real-Time Water Demand Management , 2016, Int. J. Comput. Intell. Syst..

[12]  C. Mark.  Coal bursts in the deep longwall mines of the United States , 2016 .

[13]  Dominik Slezak,et al.  Rough Set Tools for Practical Data Exploration , 2015, RSKT.

[14]  Dominik Slezak,et al.  Computation of Approximate Reducts with Dynamically Adjusted Approximation Threshold , 2015, ISMIS.

[15]  Diego Cabrera,et al.  Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal , 2015, Sensors.

[16]  Michal Kozielski,et al.  A Meta-Learning Approach to Methane Concentration Value Prediction , 2015, BDAS.

[17]  Igor Svrkota,et al.  Risk assessment model of mining equipment failure based on fuzzy logic , 2014, Expert Syst. Appl..

[18]  Francisco Herrera,et al.  Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets" , 2014, Inf. Sci..

[19]  Donato Malerba,et al.  Dealing with temporal and spatial correlations to classify outliers in geophysical data streams , 2014, Inf. Sci..

[20]  Christos Goumopoulos,et al.  Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support , 2014 .

[21]  Yu Zhang,et al.  An Integrated Environment Monitoring System for Underground Coal Mines—Wireless Sensor Network Subsystem with Multi-Parameter Monitoring , 2014, Sensors.

[22]  Xindong Wu,et al.  A NEW SUPERVISED FEATURE SELECTION METHOD FOR PATTERN CLASSIFICATION , 2014, Comput. Intell..

[23]  A. Bifet,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[24]  M. Hofmann,et al.  RapidMiner: Data Mining Use Cases and Business Analytics Applications , 2013 .

[25]  Brett Lantz,et al.  Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications , 2013 .

[26]  Charu C. Aggarwal,et al.  Managing and Mining Sensor Data , 2013, Springer US.

[27]  Pedro M. Domingos.  A few useful things to know about machine learning , 2012, Commun. ACM.

[28]  Sinh Hoa Nguyen,et al.  Machine Learning for Traffic Prediction , 2012, Fundam. Informaticae.

[29]  Marek Sikora,et al.  Improving prediction models applied in systems monitoring natural hazards and machinery , 2012, Int. J. Appl. Math. Comput. Sci..

[30]  Mi Zhang,et al.  Motion primitive-based human activity recognition using a bag-of-features approach , 2012, IHI '12.

[31]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[32]  Gavin Brown,et al.  Learn++.MF: A random subspace approach for the missing feature problem , 2010, Pattern Recognit..

[33]  Thibault Helleputte,et al.  Robust biomarker identification for cancer diagnosis with ensemble feature selection methods , 2010, Bioinform..

[34]  Jiang Hongkai,et al.  A sliding window feature extraction method for rotating machinery based on the lifting scheme , 2007 .

[35]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[36]  Józef Kabiesz,et al.  Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks , 2006 .

[37]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Marcin Michalak,et al.  Rule Quality Measures Settings in Classification, Regression and Survival Rule Induction - an Empirical Approach , 2016, Fundam. Informaticae.

[39]  Karol Kurach,et al.  Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks , 2015, RSFDGrC.

[40]  Adam Zagorecki,et al.  Prediction of Methane Outbreaks in Coal Mines from Multivariate Time Series Using Random Forest , 2015, RSFDGrC.

[41]  Marc Boullé,et al.  Prediction of Methane Outbreak in Coal Mines from Historical Sensor Data under Distribution Drift , 2015, RSFDGrC.

[42]  Marek Grzegorowski,et al.  Mining Data from Coal Mines: IJCRS'15 Data Challenge , 2015, RSFDGrC.

[43]  Dymitr Ruta,et al.  Self-Organized Predictor of Methane Concentration Warnings in Coal Mines , 2015, RSFDGrC.

[44]  Andreas Abecker,et al.  A Sensor and Semantic Data Warehouse for Integrated Water Resource Management , 2014, EnviroInfo.

[45]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[46]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[47]  Marek Sikora,et al.  Application of rule-based models for seismic hazard prediction in coal mines , 2014 .

[48]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[49]  Tomasz Grychowski,et al.  Hazard assessment based on fuzzy logic , 2008 .

[50]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..