Impact of Gradient Ascent and Boosting Algorithm in Classification

Basha, Syed Muzamil; Rajput, Dharmendra Singh; Vandhan, Vishnu

doi:10.22266/ijies2018.0228.05

Cited by 38 publications

(22 citation statements)

References 9 publications

(11 reference statements)

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“…More and more attention has been paid to the fault diagnosis methods based on machine learning [9,10]. In machine learning, the boost algorithm combines weakly predictive models into a strongly predictive model, which is adjusted by increasing the weight of the error samples to improve the accuracy of the algorithm [11][12][13][14]. However, the boost algorithm needs to use the lower limit of the accuracy of the weak classifier in advance and has limited application in industrial fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

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Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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“…Some researches did not calculate these values [6,16,32,33], which could actually improve performance measurement. SVM produced an average TPR and TNR of 92% and 96%, as can be seen in Table 7.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

Sabilla¹,

Sarno²,

Triyana³

2019

IJIES

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This research studied the use of sensors in an electronic-nose (e-nose) system to select the most efficient features to detect three classes of data signals. The data signals were collected from sensors that were assembled specifically for recognition of meat types. The development of e-noses has been the subject of many researches. However, researchers tend to install multiple sensors without optimizing their utilization, leading to higher costs. This research proposes three significant contributions to optimize the number of sensors to be used in e-noses, i.e. (i) tuning the threshold of the correlation coefficient; (ii) obtaining significant features by determining the optimal threshold of independent features; (iii) optimizing efficiency by considering the significant features and the accuracy of the results. The proposed efficiency method suggested that the number of sensors in the system could be reduced from 10 to 5. The optimal threshold suggested that 12 out of 20 features were correlated to each other. The highest accuracy obtained in this research was 92% for three classes of meats.

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“…Clustering introduces to the grouping of related objects [17] in such a way that groups of objects in the same group.…”

Section: B Clusteringmentioning

confidence: 99%

A Pattern Storage System using Pattern Warehouse along with Sources of Pattern Generation and Applications

2019

IJITEE

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Now a day different data mining algorithms are ready to create the specific set of data known as Pattern from a huge data repository, but there is no infrastructure or system to save it as persistent storage for the generated patterns. Pattern warehouse presents a foundation to make these patterns safe in the specific environment for long term use. Most organizations are excited to know the information or patterns rather than raw data or group of unprocessed data. Because extracted knowledge play a vital role to take right decision for the growth of an organization. We have examined the sources of patterns generated from large data sets. In this paper, we have presented little importance on the application area of pattern and idea of patter warehouse, the architecture of pattern warehouse then correlation between data warehouse and data mining, association between data mining and pattern warehouse, critical evaluation between existing approaches which theoretically published and more stress on association rule related review elements. In this paper, we analyze the patterns warehouse, data warehouse concerning various factors like storage space, type of storage unit, characteristics, and provide several research domains.

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Impact of Gradient Ascent and Boosting Algorithm in Classification

Cited by 38 publications

References 9 publications

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

A Pattern Storage System using Pattern Warehouse along with Sources of Pattern Generation and Applications

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