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According to Docker best practices a Docker container should not be up if the process fails to load properly. Apache Tomcat deploys all the web applications loaded in its webapp folder. This is a resilient solution for web applications not influencing the application server. However, In a Docker environment according to best practice, the process should fail in case the web application failed to initialize properly. By adding the additional jar created by this project to Tomcat classpath and adding the new listener in the Tomcat conf/server.xml the user can make sure the Tomcat process fails in case there is an initialization exception in their web application which will cause the Tomcat used the tomcat container to abort in immediately indicate that there was an initialization error Abort tomcat when a webapp fails to deploy in Docker container Docker container best practice. Tomcat does not stop if a webapp fail to deploy and the container will continue to run. By adding this listener the tomcat will abort in case the Tomcat fails to deploy the webapp, And the container will not start. You will need to place the listener in Tomcat server.xml and set the Tomcat classpath to contain this jar

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.