Outlier Prediction Using Random Forest Classifier

Mohandoss, Divya Pramasani; Shi, Yong; Suo, Kun

doi:10.1109/ccwc51732.2021.9376077

Cited by 19 publications

(10 citation statements)

References 6 publications

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“…In order to build tree-based models, such as RF, it is necessary to take samples from the dataset, select fewer attributes, and identify the value that best splits the dataset. [16] Different decision trees, such as the ones in figure 6, are combined to form an RF classifier. The wisdom of crowds, a straightforward yet potent theory, is the foundation of RF, and it states that when many unrelated individuals participate as a committee to make a forecast, the outcome is more likely to be accurate than if only one person made it.…”

Section: Methodsmentioning

confidence: 99%

Development of Harumanis Mango Insidious Fruit Rot (IFR) Detection by Utilising Vibration-Based Sensors and PCA with Random Forest

Salleh,

Abdullah,

Sudin

et al. 2023

J. Phys.: Conf. Ser.

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Utilising single or multiple modalities systems, non-destructive techniques have been used to assess and determine the quality of mango (magnifera indica L.). It is challenging to anticipate and varies by cultivar at what harvest maturity stage will result in the optimum postharvest quality. Insidious Fruit Rot (IFR) is a disease that affects mangoes. When infected with Insidious Fruit Rot (IFR), the mango variety Harumanis does not exhibit exterior mutilation at the time of harvest or during the mature stage. However, a lack of density in the sinus area can occasionally be detected. Traditional ways of locating the diseases or pests living in the mango are useless for the commercialization of the product. This research presents the investigation done on IFR infection detection using piezoelectric vibration sensors and electret microphones. Data derived by the sensors were processed using the PCA and Random Forest methods to determine the non-IFR and the mango afflicted with IFR. The proposed approach achieved correct classification and is expected to be useful for planters in detecting IFR correctly before Harumanis mangoes were marketed.

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Section: Methodsmentioning

confidence: 99%

Development of Harumanis Mango Insidious Fruit Rot (IFR) Detection by Utilising Vibration-Based Sensors and PCA with Random Forest

Salleh,

Abdullah,

Sudin

et al. 2023

J. Phys.: Conf. Ser.

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“…Random forests (RF) are a type of ensemble learning method for classification, regression, and other problems that work by building a large number of DT during training [23]. Regression and classification are only two of the many issues that can be solved using the potent ML method known as RF.…”

Section: K-neighbors Classifiermentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.

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“…Supervised outlier detection methods require the labeling of anomalous training data. Examples are given by [23,37] who train a RF classifier on labeled outliers. In contrast, unsupervised methods do not require the labeling of anomalies.…”

Section: Related Workmentioning

confidence: 99%

RODD: Robust Outlier Detection in Data Cubes

Kuhlmann¹,

Wilmes²,

Müller³

et al. 2023

Preprint

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Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cubes has not yet been treated extensively. In this work, we provide the first framework to evaluate robust outlier detection methods in data cubes (RODD). We introduce a novel random forest-based outlier detection approach (RODD-RF) and compare it with more traditional methods based on robust location estimators. We propose a general type of test data and examine all methods in a simulation study. Moreover, we apply ROOD-RF to real world data. The results show that RODD-RF can lead to improved outlier detection.

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Outlier Prediction Using Random Forest Classifier

Cited by 19 publications

References 6 publications

Development of Harumanis Mango Insidious Fruit Rot (IFR) Detection by Utilising Vibration-Based Sensors and PCA with Random Forest

Development of Harumanis Mango Insidious Fruit Rot (IFR) Detection by Utilising Vibration-Based Sensors and PCA with Random Forest

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

RODD: Robust Outlier Detection in Data Cubes

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