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  2. k-nearest neighbors algorithm - Wikipedia

    en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

    Compute the Euclidean or Mahalanobis distance from the query example to the labeled examples. Order the labeled examples by increasing distance. Find a heuristically optimal number k of nearest neighbors, based on RMSE. This is done using cross validation. Calculate an inverse distance weighted average with the k-nearest multivariate neighbors.

  3. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Three broad categories of anomaly detection techniques exist. [1] Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier. However, this approach is rarely used in anomaly detection due to the general unavailability of labelled data and the inherent ...

  4. Local outlier factor - Wikipedia

    en.wikipedia.org/wiki/Local_outlier_factor

    In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection

  6. One-class classification - Wikipedia

    en.wikipedia.org/wiki/One-class_classification

    The term one-class classification (OCC) was coined by Moya & Hush (1996) [8] and many applications can be found in scientific literature, for example outlier detection, anomaly detection, novelty detection. A feature of OCC is that it uses only sample points from the assigned class, so that a representative sampling is not strictly required for ...

  7. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.

  8. Isolation forest - Wikipedia

    en.wikipedia.org/wiki/Isolation_forest

    Isolation Forest is an algorithm for data anomaly detection using binary trees.It was developed by Fei Tony Liu in 2008. [1] It has a linear time complexity and a low memory use, which works well for high-volume data.

  9. Weak supervision - Wikipedia

    en.wikipedia.org/wiki/Weak_supervision

    Graph-based methods for semi-supervised learning use a graph representation of the data, with a node for each labeled and unlabeled example. The graph may be constructed using domain knowledge or similarity of examples; two common methods are to connect each data point to its nearest neighbors or to examples within some distance .