Java collections framework

Abstract: In this paper we report on the features of the Java Commodity Grid Kit. The Java CoG Kit provides middleware for accessing Grid functionality from the Java framework. Java CoG Kit middleware is general enough to design a variety of advanced Grid applications with quite different user requirements. Access to the Grid is established via Globus protocols, allowing the Java CoG Kit to communicate also with the C Globus reference implementation. Thus, the Java CoG Kit provides Grid developers with the ability to utilize the Grid, as well as numerous additional libraries and frameworks developed by the Java community to enable network, Internet, enterprise, and peer-to peer computing. A variety of projects have successfully used the client libraries of the Java CoG Kit to access Grids driven by the C Globus software. In this paper we also report on the efforts to develop server side Java CoG Kit components. As part of this research we have implemented a prototype pure Java resource management system that enables one to run Globus jobs on platforms on which a Java virtual machine is supported, including Windows NT machines.

Abstract: This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source Java framework (GPLv3 license) that provides a number of modules to perform a wide variety of data mining tasks. It includes tools to performdata management, design of multiple kind of experiments, statistical analyses, etc. This framework also contains KEEL-dataset, a data repository for multiple learning tasks featuring data partitions and algorithms’ results over these problems. In this work, we describe the most recent components added to KEEL 3.0, including new modules for semi-supervised learning, multi-instance learning, imbalanced classification and subgroup discovery. In addition, a new interface in R has been incorporated to execute algorithms included in KEEL. These new features greatly improve the versatility of KEEL to deal with more modern data mining problems.

Abstract: Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.

Abstract: This paper introduces jMetal, an object-oriented Java-based framework aimed at facilitating the development of metaheuristics for solving multi-objective optimization problems (MOPs). jMetal provides a rich set of classes which can be used as the building blocks of multi-objective metaheuristics; thus, taking advantage of code-reusing, the algorithms share the same base components, such as implementations of genetic operators and density estimators, so making the fair comparison of different metaheuristics for MOPs possible. The framework also incorporates a significant set of problems used as a benchmark in many comparative studies. We have implemented a number of multi-objective metaheuristics using jMetal, and an evaluation of these techniques using our framework is presented.