Predicting Dangerous Seismic Events: AAIA'16 Data Mining Challenge

Janusz, Andrzej; Ślęzak, Dominik; Sikora, Marek; Wróbel, Łukasz

doi:10.15439/2016f560

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…The series started in 2014 with the AAIA'14 Data Mining Competition: Key Risk Factors for Polish State Fire Service [8]. Other competition topics included the recognition of firefighters' activities based on inertial sensor readings [9], predicting seismic activity in coal mines [10], predicting video game winners based on game logs [11], marking hair follicles on microscopic images 5 , predicting win-rates of custom card decks in collectible card games [12], [13], and predicting typical patterns in network device workloads [14]. All of these competitions were highly successful.…”

Section: The History Of Knowledgepitmentioning

confidence: 99%

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

Janusz¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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We discuss the benefits of integrating the Knowl-edgePit data science competition platform with the BrightBox technology aimed at diagnostics of machine learning models embedded within complex software systems. We briefly recall the history of international challenges held at KnowledgePit and we also discuss in what sense such technologies as BrightBox can be helpful during the post-challenge analysis. In particular, we show how to combine solutions submitted by the competition participants in order to obtain even more accurate predictions. The discussed functionalities are of significant importance for the sponsors and organizers of data science / machine learning online contests because they support adoption of submissions while designing ultimate solutions of real-world problems.

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Section: The History Of Knowledgepitmentioning

confidence: 99%

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

Janusz¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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“…The second concerned the prediction of dangerous concentration of methane in the atmosphere of the mine sidewalks as a part of IJCRS'15 Data Challenge: Mining Data from Coal Mines [10]. The third concerned the prediction of increased seismic activity in mines as a part of AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines [9]. In all competitions, the results were very promising.…”

Section: Introductionmentioning

confidence: 99%

Massively Parallel Feature Extraction Framework Application in Predicting Dangerous Seismic Events

Grzegorowski

2016

Annals of Computer Science and Information Systems

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Abstract-In this paper we introduce an automated mechanism for knowledge discovery from data streams. As a part of this work, we also present a new approach to the creation of classifiers ensemble based on a wide variety of models. Furthermore, we describe an innovative, highly scalable feature extraction and selection framework designed to work with the MapReduce programming model and the application of designed framework to build an ensemble of classifiers which takes into account both the quality and the diversity of individual models. The effectiveness of the solution has been verified through a participation in an open data mining competition which concerned the problem of predicting periods of increased seismic activity causing lifethreatening accidents in coal mines. The submitted solution obtained the highest AUC score of all the solutions uploaded by 106 participating research teams.

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“…The task in the AAIA'16 Data Mining Competition [1] is to devise a reliable prediction model for detecting periods of increased seismic activity that endangers miners working underground in coal mines. The data set consists of hourly aggregated readings from seismic sensors that count the number of seismic bumps perceived at longwalls and measure their total energy.…”

Section: Introductionmentioning

confidence: 99%

Automatic Feature Engineering for Prediction of Dangerous Seismic Activities in Coal Mines

Zdravevski

Lameski

Kulakov

2016

Annals of Computer Science and Information Systems

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Abstract-In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature extraction from time series data that did not require any manual tuning. Furthermore, we have analyzed the impact of overlapping of input data on model robustness. We argue that training an ensemble of classifiers with distinct (i.e. nonoverlapping) chronological data rather than one classifier with all available data can produce more reliable and robust prediction models. By doing that, we were able to avoid overfitting and obtain the same score performance on the evaluation and test datasets, despite the significant data drift in the datasets.

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Predicting Dangerous Seismic Events: AAIA'16 Data Mining Challenge

Cited by 11 publications

References 12 publications

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

Massively Parallel Feature Extraction Framework Application in Predicting Dangerous Seismic Events

Automatic Feature Engineering for Prediction of Dangerous Seismic Activities in Coal Mines

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