Efficient feature size reduction via predictive forward selection

Reif, Matthias

doi:10.1016/j.patcog.2013.10.009

Cited by 59 publications

(32 citation statements)

References 20 publications

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“…The main assumption during feature selection is that the initial data set contains variables which are redundant, strongly correlated with one another or irrelevant and, therefore, can be deleted without information losses [10,11].…”

Section: Feature Selectionmentioning

confidence: 99%

Detection of Malfunctions and Abnormal Working Conditions of a Coal Mill

Kurek

Wojdan

Nabagło³

et al. 2018

Thermal Power Plants - New Trends and Recent Developments

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Coal mill malfunctions are some of the most common causes of failing to keep the power plant crucial operating parameters or even unplanned power plant shutdowns. Therefore, an algorithm has been developed that enable online detection of abnormal conditions and malfunctions of an operating mill. Based on calculated diagnostic signals and defined thresholds, this algorithm informs about abnormal operating conditions. Diagnostic signals represent the difference between the measured and the modeled values of two selected mill operating parameters. Models of mill motor current and outlet temperature of pulverized fuel were developed based on the linear regression theory. Various data analysis and feature selection procedures have been performed to obtain the best possible model. The model based on linear regression has been compared with two alternative models. The algorithm validation was carried out based on historical data containing values of operating parameters from 10 months of mill operation. Historical data were downloaded from distributed control system (DCS) of a 200-MW coal-fired power plant. Tests carried out on historical data show that this algorithm can be successfully used to detect certain abnormal conditions and malfunctions of the operating mill, such as feeder blockage, lack of coal and mill overload.

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Section: Feature Selectionmentioning

confidence: 99%

Detection of Malfunctions and Abnormal Working Conditions of a Coal Mill

Kurek

Wojdan

Nabagło³

et al. 2018

Thermal Power Plants - New Trends and Recent Developments

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“…Recent work presents algorithms faster than SFS [3], [4], [5], [6], [7]. These methods commonly try to reduce the number of wrapper evaluations.…”

Section: Hybrid Ranking and Wrapper Feature Selection Algorithmmentioning

confidence: 99%

“…Two of the cited approaches merge filter and wrapper techniques [3], [5]. Another uses predictions based on meta-features [6]. The algorithm presented in [7] uses a stochastic approach, and the number of wrapper evaluations changes according to its parameters.…”

Section: Hybrid Ranking and Wrapper Feature Selection Algorithmmentioning

confidence: 99%

“…Feature selection algorithms aim to speed up processing and augment the performance of the final classifiers [2]. In recent research, the interest to increase feature selection speed has grown [3], [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Wrapper approaches commonly generate better classifiers since they unify selection and performance evaluation. Various works [3], [5], [6], [7] try to reduce the number of wrapper evaluations to increase the algorithm velocity. The new approach presented in this work blend a wrapper and ranking approach to reduce the number of wrapper evaluations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast feature selection using hybrid ranking and wrapper approach for automatic fault diagnosis of motorpumps based on vibration signals

Boldt¹,

Rauber²,

Varejão³

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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This work presents a novel hybrid approach for feature selection using a combination of ranking and wrapper methods. Its main goal is to select features quickly, without significant loss of classification performance. Experiments comparing this approach with Sequential Forward Feature (SFS) selection showed its viability using Support Vector Machine and K-Nearest Neighbor classifiers in specific scenarios. As a test bed, vibrational signals were employed which need a previous feature extraction stage to create a classification system. In two experiments, 74 and 130 features were extracted from these databases. The proposed approach performed at least ten times faster than SFS, with 0.32% loss of accuracy in the worst case, requiring 26% to 57.5% less features to achieve its highest accuracy.

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Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Seyyedi

Minaei-Bidgoli

2018

Int J Communication

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One of the difficult challenges facing data miners is that algorithm performance degrades if the feature space contains redundant or irrelevant features. Therefore, as a critical preprocess task, dimension reduction is used to build a smaller space containing valuable features. There are 2 different approaches for dimension reduction: feature extraction and feature selection, which itself is divided into wrapper and filter approaches. In high-dimensional spaces, feature extrac-

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Efficient feature size reduction via predictive forward selection

Cited by 59 publications

References 20 publications

Detection of Malfunctions and Abnormal Working Conditions of a Coal Mill

Detection of Malfunctions and Abnormal Working Conditions of a Coal Mill

Fast feature selection using hybrid ranking and wrapper approach for automatic fault diagnosis of motorpumps based on vibration signals

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

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