Metadata-Version: 1.1
Name: pyod
Version: 0.6.7
Summary: A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)
Home-page: https://github.com/yzhao062/pyod
Author: Yue Zhao
Author-email: yuezhao@cs.toronto.edu
License: UNKNOWN
Download-URL: https://github.com/yzhao062/pyod/archive/master.zip
Description: Python Outlier Detection (PyOD)
        ===============================
        
        **Deployment & Documentation & Stats**
        
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        -----
        
        **Build Status & Code Coverage & Maintainability**
        
        
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        -----
        
        PyOD is a comprehensive and scalable **Python toolkit** for **detecting outlying objects** in 
        multivariate data. This exciting yet challenging field is commonly referred as 
        `Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
        or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
        Since 2017, PyOD has been successfully used in various academic researches and
        commercial products [#Zhao2018DCSO]_ [#Zhao2018XGBOD]_ [#Zhao2019LSCP]_.
        PyOD is featured for:
        
        
        * **Unified APIs, detailed documentation, and interactive examples** across various algorithms.
        * **Advanced models**\ , including **Neural Networks/Deep Learning** and **Outlier Ensembles**.
        * **Optimized performance with JIT and parallelization** when possible, using `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_.
        * **Compatible with both Python 2 & 3** (scikit-learn compatible as well).
        
        **Important Notes**\ :
        PyOD contains some neural network based models, e.g., AutoEncoders, which are
        implemented in keras. However, PyOD would **NOT** install **Keras** and/or **TensorFlow** automatically. This
        reduces the risk of damaging your local installations. 
        So you should install keras and back-end libraries like TensorFlow, if you want
        to use neural net based models. An instruction is provided: `neural-net FAQ <https://github.com/yzhao062/pyod/wiki/Setting-up-Keras-and-Tensorflow-for-Neural-net-Based-models>`_.
        Similarly, some models depend on **xgboost**, which would **NOT** be installed by default.
        
        **Key Links and Resources**\ :
        
        
        * `View the latest codes on Github <https://github.com/yzhao062/pyod>`_
        * `Execute Interactive Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_
        * `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_
        
        
        **Table of Contents**\ :
        
        
        * `Quick Introduction <#quick-introduction>`_
        * `Installation <#installation>`_
        * `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
        * `Algorithm Benchmark <#algorithm-benchmark>`_
        * `Quick Start for Outlier Detection <#quick-start-for-outlier-detection>`_
        * `Quick Start for Combining Outlier Scores from Various Base Detectors <#quick-start-for-combining-outlier-scores-from-various-base-detectors>`_
        * `How to Contribute and Collaborate <#how-to-contribute-and-collaborate>`_
        
        ----
        
        
        Quick Introduction
        ^^^^^^^^^^^^^^^^^^
        
        PyOD toolkit consists of three major groups of functionalities:
        
        **(i) Individual Detection Algorithms** :
        
        ===================  ================  ======================================================================================================  =====  ========================================
        Type                 Abbr              Algorithm                                                                                               Year   Ref
        ===================  ================  ======================================================================================================  =====  ========================================
        Linear Model         PCA               Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)   2003   [#Shyu2003A]_
        Linear Model         MCD               Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)                    1999   [#Hardin2004Outlier]_ [#Rousseeuw1999A]_
        Linear Model         OCSVM             One-Class Support Vector Machines                                                                       2003   [#Ma2003Time]_
        Proximity-Based      LOF               Local Outlier Factor                                                                                    2000   [#Breunig2000LOF]_
        Proximity-Based      CBLOF             Clustering-Based Local Outlier Factor                                                                   2003   [#He2003Discovering]_
        Proximity-Based      LOCI              LOCI: Fast outlier detection using the local correlation integral                                       2003   [#Papadimitriou2003LOCI]_
        Proximity-Based      HBOS              Histogram-based Outlier Score                                                                           2012   [#Goldstein2012Histogram]_
        Proximity-Based      kNN               k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score                  2000   [#Ramaswamy2000Efficient]_
        Proximity-Based      AvgKNN            Average kNN (use the average distance to k nearest neighbors as the outlier score)                      2002   [#Angiulli2002Fast]_
        Proximity-Based      MedKNN            Median kNN (use the median distance to k nearest neighbors as the outlier score)                        2002   [#Angiulli2002Fast]_
        Probabilistic        ABOD              Angle-Based Outlier Detection                                                                           2008   [#Kriegel2008Angle]_
        Probabilistic        FastABOD          Fast Angle-Based Outlier Detection using approximation                                                  2008   [#Kriegel2008Angle]_
        Probabilistic        SOS               Stochastic Outlier Selection                                                                            2012   [#Janssens2012Stochastic]_
        Outlier Ensembles    IForest           Isolation Forest                                                                                        2008   [#Liu2008Isolation]_
        Outlier Ensembles                      Feature Bagging                                                                                         2005   [#Lazarevic2005Feature]_
        Outlier Ensembles    LSCP              LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                       2019   [#Zhao2019LSCP]_
        Outlier Ensembles    XGBOD             Extreme Boosting Based Outlier Detection **(Supervised)**                                               2018   [#Zhao2018XGBOD]_
        Neural Networks      AutoEncoder       Fully connected AutoEncoder (use reconstruction error as the outlier score)                                    [#Aggarwal2015Outlier]_ [Ch.3]
        Neural Networks      SO_GAAL           Single-Objective Generative Adversarial Active Learning                                                 2019   [#Liu2018Generative]_
        Neural Networks      MO_GAAL           Multiple-Objective Generative Adversarial Active Learning                                               2019   [#Liu2018Generative]_
        ===================  ================  ======================================================================================================  =====  ========================================
        
        
        **(ii) Outlier Ensembles & Outlier Detector Combination Frameworks**:
        
        ===================  ================  =====================================================================================================  =====  ========================================
        Type                 Abbr              Algorithm                                                                                              Year   Ref
        ===================  ================  =====================================================================================================  =====  ========================================
        Outlier Ensembles                      Feature Bagging                                                                                        2005   [#Lazarevic2005Feature]_
        Outlier Ensembles    LSCP              LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                      2019   [#Zhao2019LSCP]_
        Combination          Average           Simple combination by averaging the scores                                                             2015   [#Aggarwal2015Theoretical]_
        Combination          Weighted Average  Simple combination by averaging the scores with detector weights                                       2015   [#Aggarwal2015Theoretical]_
        Combination          Maximization      Simple combination by taking the maximum scores                                                        2015   [#Aggarwal2015Theoretical]_
        Combination          AOM               Average of Maximum                                                                                     2015   [#Aggarwal2015Theoretical]_
        Combination          MOA               Maximization of Average                                                                                2015   [#Aggarwal2015Theoretical]_
        ===================  ================  =====================================================================================================  =====  ========================================
        
        
        **(iii) Utility Functions**:
        
        ===================  ==================  =====================================================================================================================================================  ==========================================================================================================================
        Type                 Name                Function                                                                                                                                               Documentation
        ===================  ==================  =====================================================================================================================================================  ==========================================================================================================================
        Data                 generate_data       Synthesized data generation; normal data is generated by a multivariate Gaussian and outliers are generated by a uniform distribution                  `generate_data <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.data.generate_data>`_
        Stat                 wpearsonr           Calculate the weighted Pearson correlation of two samples                                                                                              `wpearsonr <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.stat_models.wpearsonr>`_
        Utility              get_label_n         Turn raw outlier scores into binary labels by assign 1 to top n outlier scores                                                                         `get_label_n <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.get_label_n>`_
        Utility              precision_n_scores  calculate precision @ rank n                                                                                                                           `precision_n_scores <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.precision_n_scores>`_
        ===================  ==================  =====================================================================================================================================================  ==========================================================================================================================
        
        ----
        
        Installation
        ^^^^^^^^^^^^
        
        It is recommended to use **pip** for installation. Please make sure
        **the latest version** is installed, as PyOD is updated frequently:
        
        .. code-block:: bash
        
           pip install pyod
           pip install --upgrade pyod  # make sure the latest version is installed!
           pip install --pre pyod      # or include pre-release version for new features
        
        Alternatively, install from github directly (\ **NOT Recommended**\ )
        
        .. code-block:: bash
        
           git clone https://github.com/yzhao062/pyod.git
           python setup.py install
        
        **Required Dependencies**\ :
        
        
        * Python 2.7, 3.5, 3.6, or 3.7
        * numpy>=1.13
        * numba>=0.35
        * scipy>=0.19.1
        * scikit_learn>=0.19.1
        
        **Optional Dependencies (see details below)**\ :
        
        
        * Keras (optional, required for AutoEncoder)
        * Matplotlib (optional, required for running examples)
        * Tensorflow (optional, required for AutoEncoder, other backend works)
        * XGBoost (optional, required for XGBOD)
        
        **Known Issue 1**\ : Running examples needs Matplotlib, which may throw errors in conda
        virtual environment on mac OS. See reasons and solutions `issue6 <https://github.com/yzhao062/pyod/issues/6>`_.
        
        **Known Issue 2**\ : Keras and/or TensorFlow are listed as optional. However, they are
        both required if you want to use neural network based models, such as
        AutoEncoder. See reasons and solutions `neural-net installation <https://github.com/yzhao062/pyod/wiki/Setting-up-Keras-and-Tensorflow-for-Neural-net-Based-models>`_
        
        **Known Issue 3**\ : xgboost is listed as optional. However, it is required to
        run XGBOD. Users are expected to install **xgboost** to use XGBOD model.
        
        
        ----
        
        
        API Cheatsheet & Reference
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Full API Reference: (https://pyod.readthedocs.io/en/latest/pyod.html). API cheatsheet for all detectors:
        
        
        * **fit(X)**\ : Fit detector.
        * **fit_predict(X)**\ : Fit detector first and then predict whether a particular sample is an outlier or not.
        * **fit_predict_score(X, y)**\ : Fit the detector, predict on samples, and evaluate the model by predefined metrics, e.g., ROC.
        * **decision_function(X)**\ : Predict raw anomaly score of X using the fitted detector.
        * **predict(X)**\ : Predict if a particular sample is an outlier or not using the fitted detector.
        * **predict_proba(X)**\ : Predict the probability of a sample being outlier using the fitted detector.
        
        Key Attributes of a fitted model:
        
        
        * **decision_scores**\ : The outlier scores of the training data. The higher, the more abnormal.
          Outliers tend to have higher scores.
        * **labels_**\ : The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies.
        
        Full package structure can be found below:
        
        
        * http://pyod.readthedocs.io/en/latest/genindex.html
        * http://pyod.readthedocs.io/en/latest/py-modindex.html
        
        
        ----
        
        Algorithm Benchmark
        ^^^^^^^^^^^^^^^^^^^
        
        **Comparison of all implemented models** are made available below:
        
        (\ `Figure <https://raw.githubusercontent.com/yzhao062/pyod/master/examples/ALL.png>`_\ ,
        `compare_all_models.py <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>`_\ ,
        `Interactive Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_\ ):
        
        For Jupyter Notebooks, please navigate to **"/notebooks/Compare All Models.ipynb"**
        
        
        .. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/ALL.png
           :target: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/ALL.png
           :alt: Comparision_of_All
        
        To provide an overview and quick guidance of the implemented models, a benchmark
        is supplied. In total, 17 benchmark data are used for comparision, all datasets could be
        downloaded at `ODDS <http://odds.cs.stonybrook.edu/#table1>`_.
        
        For each dataset, it is first split into 60% for training and 40% for testing.
        All experiments are repeated 20 times independently with different samplings.
        The mean of 20 trials are taken as the final result. Three evaluation metrics
        are provided:
        
        
        * The area under receiver operating characteristic (ROC) curve
        * Precision @ rank n (P@N)
        * Execution time
        
        Check the latest result `benchmark <https://pyod.readthedocs.io/en/latest/benchmark.html>`_.
        You are welcome to replicate this process by running
        `benchmark.py <https://github.com/yzhao062/pyod/blob/master/notebooks/benchmark.py>`_.
        
        ----
        
        
        Quick Start for Outlier Detection
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        See **examples directory** for more demos. `"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/knn_example.py>`_
        demonstrates the basic APIs of PyOD using kNN detector. **It is noted the APIs for other detectors are similar**.
        
        More detailed instruction of running examples can be found `examples. <https://github.com/yzhao062/pyod/blob/master/examples>`_
        
        
        #. Initialize a kNN detector, fit the model, and make the prediction.
        
           .. code-block:: python
        
        
               from pyod.models.knn import KNN   # kNN detector
        
               # train kNN detector
               clf_name = 'KNN'
               clf = KNN()
               clf.fit(X_train)
        
               # get the prediction label and outlier scores of the training data
               y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
               y_train_scores = clf.decision_scores_  # raw outlier scores
        
               # get the prediction on the test data
               y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
               y_test_scores = clf.decision_function(X_test)  # outlier scores
        
        #. Evaluate the prediction by ROC and Precision@rank *n* (p@n):
        
           .. code-block:: python
        
        
               # evaluate and print the results
               print("\nOn Training Data:")
               evaluate_print(clf_name, y_train, y_train_scores)
               print("\nOn Test Data:")
               evaluate_print(clf_name, y_test, y_test_scores)
        
        
        #. See a sample output & visualization
        
        
           .. code-block:: python
        
        
               On Training Data:
               KNN ROC:1.0, precision @ rank n:1.0
        
               On Test Data:
               KNN ROC:0.9989, precision @ rank n:0.9
        
           .. code-block:: python
        
        
               visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
                   y_test_pred, show_figure=True, save_figure=False)
        
        Visualization (\ `knn_figure <https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png>`_\ ):
        
        .. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
           :target: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
           :alt: kNN example figure
        
        
        ----
        
        Quick Start for Combining Outlier Scores from Various Base Detectors
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        "examples/comb_example.py" illustrates the APIs for combining multiple base detectors
        (\ `comb_example.py <https://github.com/yzhao062/pyod/blob/master/examples/comb_example.py>`_\ ,
        `Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_\ ).
        
        For Jupyter Notebooks, please navigate to **"/notebooks/Model Combination.ipynb"**
        
        Given we have *n* individual outlier detectors, each of them generates an individual score for all samples.
        The task is to combine the outputs from these detectors effectively
        **Key Step: conducting Z-score normalization on raw scores before the combination.**
        Four combination mechanisms are shown in this demo:
        
        
        #. Average: take the average of all base detectors.
        #. maximization : take the maximum score across all detectors as the score.
        #. Average of Maximum (AOM): first randomly split n detectors in to p groups. For each group, use the maximum within the group as the group output. Use the average of all group outputs as the final output.
        #. Maximum of Average (MOA): similarly to AOM, the same grouping is introduced. However, we use the average of a group as the group output, and use maximum of all group outputs as the final output.
           To better understand the merging techniques, refer to [6].
        
        The walkthrough of the code example is provided:
        
        
        #. Import models and generate sample data
        
           .. code-block:: python
        
               from pyod.models.knn import KNN
               from pyod.models.combination import aom, moa, average, maximization
               from pyod.utils.data import generate_data
        
               X, y = generate_data(train_only=True)  # load data
        
        #. First initialize 20 kNN outlier detectors with different k (10 to 200), and get the outlier scores:
        
           .. code-block:: python
        
               # initialize 20 base detectors for combination
               k_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,
                           150, 160, 170, 180, 190, 200]
        
               train_scores = np.zeros([X_train.shape[0], n_clf])
               test_scores = np.zeros([X_test.shape[0], n_clf])
        
               for i in range(n_clf):
                   k = k_list[i]
        
                   clf = KNN(n_neighbors=k, method='largest')
                   clf.fit(X_train_norm)
        
                   train_scores[:, i] = clf.decision_scores_
                   test_scores[:, i] = clf.decision_function(X_test_norm)
        
        #. Then the output codes are standardized into zero mean and unit variance before combination.
        
        
           .. code-block:: python
        
               from pyod.utils.utility import standardizer
               train_scores_norm, test_scores_norm = standardizer(train_scores, test_scores)
        
        #. Then four different combination algorithms are applied as described above:
        
           .. code-block:: python
        
               comb_by_average = average(test_scores_norm)
               comb_by_maximization = maximization(test_scores_norm)
               comb_by_aom = aom(test_scores_norm, 5) # 5 groups
               comb_by_moa = moa(test_scores_norm, 5)) # 5 groups
        
        #. Finally, all four combination methods are evaluated with ROC and Precision @ Rank n:
        
           .. code-block:: bash
        
               Combining 20 kNN detectors
               Combination by Average ROC:0.9194, precision @ rank n:0.4531
               Combination by Maximization ROC:0.9198, precision @ rank n:0.4688
               Combination by AOM ROC:0.9257, precision @ rank n:0.4844
               Combination by MOA ROC:0.9263, precision @ rank n:0.4688
        
        ----
        
        How to Contribute and Collaborate
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        You are welcome to contribute to this exciting project, and a manuscript at
        `JMLR <http://www.jmlr.org/mloss/>`_ (Track for open-source software) is under review.
        
        If you are interested in contributing:
        
        
        * Please first check Issue lists for "help wanted" tag and comment the one
          you are interested. We will assign the issue to you.
        
        * Fork the master branch and add your improvement/modification/fix.
        
        * Create a pull request and follow the pull request template `PR template <https://github.com/yzhao062/pyod/blob/master/PULL_REQUEST_TEMPLATE.md>`_
        
        
        To make sure the code has the same style and standard, please refer to models,
        such as abod.py, hbos.py, or feature bagging for example.
        
        You are also welcome to share your ideas by opening an issue or dropping me
        an email at yuezhao@cs.toronto.edu :)
        
        
        ----
        
        Reference
        ^^^^^^^^^
        
        
        .. [#Aggarwal2015Outlier] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham.
        
        .. [#Aggarwal2015Theoretical] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ *ACM SIGKDD Explorations Newsletter*\ , 17(1), pp.24-47.
        
        .. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27.
        
        .. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM Sigmod Record*\ , 29(2), pp. 93-104.
        
        .. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*\ , pp.59-63.
        
        .. [#Hardin2004Outlier] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*\ , 44(4), pp.625-638.
        
        .. [#He2003Discovering] He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. *Pattern Recognition Letters*\ , 24(9-10), pp.1641-1650.
        
        .. [#Janssens2012Stochastic] Janssens, J.H.M., Huszár, F., Postma, E.O. and van den Herik, H.J., 2012. Stochastic outlier selection. Technical report TiCC TR 2012-001, Tilburg University, Tilburg Center for Cognition and Communication, Tilburg, The Netherlands.
        
        .. [#Kriegel2008Angle] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*\ , pp. 444-452. ACM.
        
        .. [#Lazarevic2005Feature] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005.
        
        .. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE.
        
        .. [#Liu2018Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2018. Generative Adversarial Active Learning for Unsupervised Outlier Detection. arXiv preprint arXiv:1809.10816.
        
        .. [#Ma2003Time] Ma, J. and Perkins, S., 2003, July. Time-series novelty detection using one-class support vector machines. In *IJCNN' 03*\ , pp. 1741-1745. IEEE.
        
        .. [#Papadimitriou2003LOCI] Papadimitriou, S., Kitagawa, H., Gibbons, P.B. and Faloutsos, C., 2003, March. LOCI: Fast outlier detection using the local correlation integral. In *ICDE '03*, pp. 315-326. IEEE.
        
        .. [#Ramaswamy2000Efficient] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM Sigmod Record*\ , 29(2), pp. 427-438).
        
        .. [#Rousseeuw1999A] Rousseeuw, P.J. and Driessen, K.V., 1999. A fast algorithm for the minimum covariance determinant estimator. *Technometrics*\ , 41(3), pp.212-223.
        
        .. [#Shyu2003A] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*.
        
        .. [#Zhao2018DCSO] Zhao, Y. and Hryniewicki, M.K. DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles. *ACM SIGKDD Workshop on Outlier Detection De-constructed (ODD v5.0)*\ , 2018.
        
        .. [#Zhao2018XGBOD] Zhao, Y. and Hryniewicki, M.K. XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. *IEEE International Joint Conference on Neural Networks*\ , 2018.
        
        .. [#Zhao2019LSCP] Zhao, Y., Hryniewicki, M.K., Nasrullah, Z., and Li, Z. LSCP: Locally Selective Combination of Parallel Outlier Ensembles. *SIAM International Conference on Data Mining (SDM)*. 2019. **Accepted, to appear**.
        
Keywords: outlier detection,anomaly detection,outlier ensembles,data mining,neural networks
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
