Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Meina, Michał; Janusz, Andrzej; Rykaczewski, K. P.; Ślęzak, Dominik; Celmer, Bartosz; Krasuski, Adam

doi:10.15439/2015f426

Cited by 23 publications

(19 citation statements)

References 15 publications

(17 reference statements)

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“…A recent data mining competition for posture recognition of firefighters [2] inspired different feature engineering approaches that are very effective [3,4,5]. Using the proposed approaches there, from each series of readings the system generates the following types of features: basic statistics (minimum, maximum, range, arithmetic mean, harmonic mean, geometric mean, median, mode, standard deviation, variance, skewness, kurtosis, signal-to-noise ratio, energy, etc.…”

Section: A Feature Generatorsmentioning

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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Section: A Feature Generatorsmentioning

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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“…The topic of the AAIA'15 Data Mining Competition [5] was Tagging Firefighter Activities at a Fire Scene. In particular, the task is related to the problem of recognizing activities carried out by firefighters based on streams of information from body sensor networks.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The research presented in these papers aims to increase the firefighter safety by monitoring their kinematics and psychophysical condition during the course of fire and rescue actions. The following paragraph is extracted from the competition website [5] and describes the task in more detail.…”

Section: Problem Descriptionmentioning

confidence: 99%

Robust histogram-based feature engineering of time series data

Zdravevski

Lameski

Mingov³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is three-fold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.

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“…One of its most fundamental components relies on a proper detection of an action a fireman is actually performing at a given moment. The topic of AAIA 2015 Data Mining Competition: Tagging firefighters' activities at a fire scene [8] aimed to deliver accurate model for recognising firefighters movements and activities based on multi-sensor data. In consecutive sections we explain the winning approach in very detail.…”

Section: Introductionmentioning

confidence: 99%

The Winning Solution to the AAIA'15 Data Mining Competition: Tagging Firefighter Activities at a Fire Scene

Lašek

Gągolewski

2015

Annals of Computer Science and Information Systems

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Abstract-Multi-sensor based classification of professionals' activities plays a key role in ensuring the success of an his/her goals. In this paper we present the winning solution to the AAIA'15 Tagging Firefighter Activities at a Fire Scene data mining competition. The approach is based on a Random Forest classifier trained on an input data set with almost 5000 features describing the underlying time series of sensory data.

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Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Cited by 23 publications

References 15 publications

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

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

Robust histogram-based feature engineering of time series data

The Winning Solution to the AAIA'15 Data Mining Competition: Tagging Firefighter Activities at a Fire Scene

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