On the calibration of sensor arrays for pattern recognition using the minimal number of experiments

Rodríguez-Luján, Irene; Fonollosa, Jordi; Vergara, Alexander; Homer, M. L.; Huerta, Ramón

doi:10.1016/j.chemolab.2013.10.012

Cited by 163 publications

(104 citation statements)

References 43 publications

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“…We experiment with real contextual data from K = 16 chemical sensors exposed to three gases of three chemical compounds at a certain concentration level [33], [34]. Each sensor detects three specific environmental contextual parameters corresponding to Ethylene, Ammonia, and Toluene, respectively.…”

Section: Experiments Setupmentioning

confidence: 99%

Quality-optimized predictive analytics

Anagnostopoulos

2016

Appl Intell

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On-line statistical and machine learning analytic tasks over largescale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with qualityunaware methods over real sensory multivariate contextual data.

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Section: Experiments Setupmentioning

confidence: 99%

Quality-optimized predictive analytics

Anagnostopoulos

2016

Appl Intell

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“…We used the gas sensor dataset from the UCI Machine Learning repository [4], [19], [20]. We used the gas sensor array drift dataset at different concentrations data set.…”

Section: Time Series Data Setmentioning

confidence: 99%

Majority Rule Approach of Deep Learning for Time Series Data

Niimi¹,

Ikeda²

2016

IJISR

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“…Gas sensor array drift at different concentration dataset [11,13] is used for the whole experiment and analysis. It"s a multivariate time series dataset which contains no missing values.…”

Section: B Datasetmentioning

confidence: 99%

Sensor Drift Compensation in Time Series Prediction through Regularized Ensemble of Classifiers

Asmita¹,

Shukla²

2015

International Journal of Advanced Research in Computer and Comm

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Concept drift is a major problem that the chemical sensor community is facing in their research and development. This arises when the predictive characteristic feature of the target variable in the sensor setup, changes due to some chemical or physical interaction of the environment elements with the surface of the sensor and other factors such as aging and poisoning of the surface. This is not a new problem but is having a wide scope of further developments. Recent research suggests an upcoming solution for this drift compensation as ensemble of classifiers with uniform weightage to all the participating classifiers or some non-uniform weightage according to performance of individual classifier. There arise some ill posed problems with this ensemble of classifiers. This paper introduces a new machine learning approach to solve this problem through regularized weighted ensemble of classifiers for overcoming the time dependent drift occurrence. The weights are chosen by regularized majority voting which are then associated with the individual classifiers to form the ensembles. This regularized drift compensation algorithm is applied to solve the gas discrimination problem for classification of 6 different volatile organic compounds over time series data set consisting of data recorded using an array of 16 metal oxides sensors for 3 years. Our experiment tries various different regularization approaches and finds best improvement in case of double regularization of Single Value Decomposition and L2 regularization. Results clearly indicate improved classification accuracy in presence of drift and better results than recent reporting. To the best of our knowledge, such a machine learning approach has not yet been applied for drift correction in sensor community.

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On the calibration of sensor arrays for pattern recognition using the minimal number of experiments

Cited by 163 publications

References 43 publications

Quality-optimized predictive analytics

Quality-optimized predictive analytics

Majority Rule Approach of Deep Learning for Time Series Data

Sensor Drift Compensation in Time Series Prediction through Regularized Ensemble of Classifiers

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