Data mining with Rattle and R

In this paper, recently developed variants of wavelet transform, namely the maximum overlapping discrete wavelet transform and the second-generation wavelet transform, are used for detection of ten types of the power quality (PQ) disturbance signals. Further, the features of PQ signal disturbances are extracted using these wavelet transforms. Those extracted features are then used to classify various PQ disturbances. Random forest (RF) classifier is presented in this paper. The RF is constructed with multiple trees for classification of large number of classes simultaneously. In order to represent realistic situation, the proposed technique is tested with noisy data.Keywords Power quality disturbances (PQD ) · Maximal overlap discrete wavelet transform (MODWT ) · Secondgeneration wavelet transform (SGWT ) · Random forest (RF ) · Power quality (PQ ) · Classification accuracy (CA )

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“…. , J is the scale number (Mwitondi 2013;Percival and Walden 2006). The representation of MODWT is shown in Fig.…”

Section: Maximum Overlapping Discrete Wavelet Transform (Modwt)mentioning

confidence: 99%

Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

Upadhyaya

Mohanty

Bhende

2015

J Control Autom Electr Syst

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“…The features from the training patterns has been implemented to construct DT [20]. The algorithm for design of DT is under given [14], [21].…”

Section: Decision Tree Classifier (Dt)mentioning

confidence: 99%

“…Datamining based classifier named the decision tree (DT) is suitable to discriminate PQDS. Decision Tree (DT) [14], [15] has been here chosen to discriminate the PQDS.…”

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confidence: 99%

Implementation of MODWT on Real Time Power Quality Disturbance Signal

Upadhyaya*,

Panda

2020

IJITEE

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 Abstract: Maximal overlap discrete wavelet transform (MODWT) is the upgradation of traditional wavelet transform (WT), has been employed for localization of different power quality disturbance signal (PQDS). Every signal has been break down up to fourth level to localize the disturbances. The co-efficient of MODWT have been again employed for classification. The selected indices have been obtained utilizing the detail coefficient of this variant of WT. These features are the inputs to the data mining classifier. Decision Tree (DT) have been implemented for discrimination of PQ disturbance signals. Various PQDS have been generated in noisy and noise free climate. Besides this, the aforementioned techniques is examined with three phase signals bring out from transmission line panels.

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“…The optimum number of variables randomly sampled at each split was estimated by the decrease in the out-of-bag (OOB) error, using the 'tuneRF' function implemented in the 'randomForest' package (Liaw and Wiener 2002). We assessed model performance based on several estimators that evaluated the efficiency, rate, and type of errors in the discriminatory capacity of binary models, including the OOB error, the area under the receiver-operating characteristic curve (AUC), and the rates of false positives (FP) and false negatives (FN) (Lalkhen and McCluskey 2008;Mwitondi 2013). The OOB is the misclassification rate of the random forest model estimated for the training data, with higher values indicating models having lower classification accuracy (Mwitondi 2013).…”

Section: Data Analysesmentioning

confidence: 99%

“…We assessed model performance based on several estimators that evaluated the efficiency, rate, and type of errors in the discriminatory capacity of binary models, including the OOB error, the area under the receiver-operating characteristic curve (AUC), and the rates of false positives (FP) and false negatives (FN) (Lalkhen and McCluskey 2008;Mwitondi 2013). The OOB is the misclassification rate of the random forest model estimated for the training data, with higher values indicating models having lower classification accuracy (Mwitondi 2013). The AUC facilitated evaluation of the predictive efficiency of the model; its values ranged from 0.5 for models having predictive capacity similar to chance, to 1.0 for models having perfect predictive ability (Araújo et al 2005).…”

Section: Data Analysesmentioning

confidence: 99%

Using hierarchical spatial models to assess the occurrence of an island endemism: the case of Salamandra corsica

Escoriza

Hernández

2019

Ecol Process

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Background: Island species are vulnerable to rapid extinction, so it is important to develop accurate methods to determine their occurrence and habitat preferences. In this study, we assessed two methods for modeling the occurrence of the Corsican endemic Salamandra corsica, based on macro-ecological and fine habitat descriptors. We expected that models based on habitat descriptors would better estimate S. corsica occurrence, because its distribution could be influenced by micro-environmental gradients. The occurrence of S. corsica was modeled according to two ensembles of variables using random forests. Results: Salamandra corsica was mainly found in forested habitats, with a complex vertical structure. These habitats are associated with more stable environmental conditions. The model based on fine habitat descriptors was better able to predict occurrence, and gave no false negatives. The model based on macro-ecological variables underestimated the occurrence of the species on its ecological boundary, which is important as such locations may facilitate interpopulation connectivity. Conclusions: Implementing fine spatial resolution models requires greater investment of resources, but this is advisable for study of microendemic species, where it is important to reduce type II error (false negatives).

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Data mining with Rattle and R

Cited by 5 publications

References 0 publications

Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

Hybrid Methods for Fast Detection and Characterization of Power Quality Disturbances

Implementation of MODWT on Real Time Power Quality Disturbance Signal

Using hierarchical spatial models to assess the occurrence of an island endemism: the case of Salamandra corsica

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