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Random forests
Published in Brandon M. Greenwell, Tree-Based Methods for Statistical Learning in R, 2022
While the proximities of an RF can be used to detect novelties and potential outliers, they're rather computationally expensive to compute and store, especially for large data sets; also, as previously mentioned, many RF implementations do not support proximities. A more general approach to anomaly detection, called an isolation forest, was proposed in Liu et al. [2008]. An isolation forest is essentially an ensemble of isolation trees (IsoTrees). IsoTrees are similar to extra-trees with K=1 (Section 7.8.4), except that the splitting variables are also chosen at random; hence, IsoTrees are unsupervised in the sense that the tree building process does not make use of any response variable information.
Public Perception toward AI-Driven Healthcare and the Way Forward in the Post-Pandemic Era
Published in Chinmay Chakraborty, Digital Health Transformation with Blockchain and Artificial Intelligence, 2022
Spandan Datta, Nilesh Tejrao Kate, Abhishek Srivastava
The article ‘Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems’ was reviewed and analyzed. The article was published in IEEE in 2021. The construct identified is the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), disease diagnosis model for heart disease and diabetes. The proposed technique uses a Crow Search Optimization algorithm-based Cascaded Long Short-Term Memory (CSO-CLSTM) model for illness detection. CSO is used to fine-tune the CLSTM model’s weights and bias factors to enhance medical data categorization. This study also uses the isolation Forest (iForest) approach to remove outliers. During the trials, the reported CSO-LSTM model had the highest levels of accuracy of 96.16% and 97.26% in detecting diabetes and heart disease, correspondingly. As a result, the suggested CSO-LSTM model can be used in smart healthcare systems as a disease diagnosis tool as suggested by the authors [33].
What Is a Needle in a Haystack?
Published in Yair Neuman, How to Find a Needle in a Haystack, 2023
Let us now use this data to explain the Isolation Forest approach. The decision tree in Figure 2.2 uses one feature only (net worth) and aims to identify each leader in our small dataset. As you can see, Putin is located at the top of the tree, while Boris Johnson and Emmanuel Macron are found at the bottom. The Isolation Forest suggests that if we build an ensemble of decision-tress for a given dataset, then the anomalies are those instances that have the shortest average path lengths on the trees. This unsupervised anomaly detection algorithm takes a non-standard approach to anomaly detection by suggesting that instead of looking for a simple deviation from a norm, we should do something else.
Behavior Analysis with Machine Learning Using R,
Published in Technometrics, 2022
Chapter 10, “Detecting Abnormal Behaviors,” is important for the fields of health care, ecology, economy, and many others, for instance, illegal bank transactions, defective products, natural disasters, etc. Anomaly detection can be trained in a binary classifier to distinguish normal behavior and outliers. The Isolation Forest model consists in generating random partitions of the features, and the average tree path length is smaller for abnormal rather than normal points. Another approach employs the autoencoders, which are ACC models with the same shape of the input and output layers, and the reconstruction error serves as an anomaly score. On the abnormal fish behavior data, the preprocessing, visualizing trajectories, feature extraction, the receiver operating characteristic (ROC) depicting the sensitivity and false positive rate (FPR), and the area under curve (AUC) for performance estimation are described.
Machine learning based fault detection approach to enhance quality control in smart manufacturing
Published in Production Planning & Control, 2023
Isolation Forest uses decision trees to find anomalies and outliers. We chose this algorithm for our investigation because we think it’s acceptable. As our method is tree-based, we could only discern abnormalities in the data. Like ANN, test and train data sets were used. The train model was run using test data and the results obtained. According to what we said, the Isolation Forest would provide a rating that is bounded among 0 and 1, with obtain a significant to 1 being regarded abnormal as well as rates 0.5 being deemed regular.
Machine learning based anomaly detection and diagnosis method of spinning equipment driven by spectrogram data
Published in The Journal of The Textile Institute, 2022
Chen Shen, Bing Chen, Lianqing Yu, Fei Fan
An isolation forest is a set of isolation trees which produce shorter paths for anomalies. For each tree, the number of iterations ht(x) required to isolate a sample can be calculated, then the average path length required to isolate a sample in the forest is expressed as