Outlier Detection with Supervised Learning Method

Bawono, Aditya Hari; Bachtiar, Fitra A.

doi:10.1109/siet48054.2019.8986101

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Unlike unsupervised models, supervised models can provide a clearer metric for performance evaluation, since they use labeled data, allowing for precise measurement of accuracy [23]. Recently, studies have also shown high efficiency for supervised outlier detection, such as the studies by Bawono and Bachtiar [24], Paulheim and Meusel [25], and Aggarwal and Aggarwal [26]. The differences and comparisons between unsupervised and supervised models regarding building science data outlier detection methods were discussed by [27].…”

Section: Supervised Outlier Detectionmentioning

confidence: 99%

“…Bawono and Bachtiar [24] addressed the challenges of using supervised learning for outlier detection, particularly the issue of imbalanced data classification due to the typically small proportion of outliers in datasets. The authors showed a promising result to resolve the current issues of supervised learning-based outlier detection.…”

Section: Supervised Outlier Detectionmentioning

confidence: 99%

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VOD: Vision-Based Building Energy Data Outlier Detection

Tian,

Zhao,

et al. 2024

MAKE

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Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings.

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Section: Supervised Outlier Detectionmentioning

confidence: 99%

Section: Supervised Outlier Detectionmentioning

confidence: 99%

VOD: Vision-Based Building Energy Data Outlier Detection

Tian,

Zhao,

et al. 2024

MAKE

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“…Interestingly, they do not rely on any prior knowledge of the anomaly pattern and it is easy to train the model. Again, this model fails in a high-dimensional space, further attributed with the local neighborhood problem [6], 3) unsupervised method: the anomalies are detected through a heuristic approach with certain assumptions of segregating the regular instances versus other data points that deviate from the cluster. K-means and DBSCAN are the prominent techniques here [7].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

Siddalingappa¹,

Sekar²

2021

IJACSA

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Detection of anomalies from the medical image dataset improves prognosis by discovering new facts hidden in the data. The present study aims to discuss anomaly detection using autoencoders and convolutional neural networks. The autoencoder identifies the imbalance between normal and abnormal samples. They create learning models flexible and accurate on training data. The problem is addressed in four stages: 1) training: an autoencoder is initialized with the hyperparameters and trained on the lung cancer CT scan images, 2) test: the autoencoder reconstructs the input from the latent space representation with a slight variation from the original data, indicated by a reconstruction error as Mean Squared Error (MSE), 3) evaluate: the MSE value of the training and test dataset are compared. The MSE values of anomalous data are higher than a base threshold, detecting those as anomalies, 4) validate: the efficiency metrics such as accuracy and MSE scores are used at both training and validation phases. The dataset was further classified as benign and malignant. The accuracy reported for outlier detection and the classification task is 98% and 97.2%. Thus, the proposed autoencoder-based anomaly detection could positively isolate anomalies from the CT scan images of lung cancer.

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