WEKA: A Machine Learning Workbench
نویسندگان
چکیده
WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learned include the necessity of providing a wealth of interactive tools for data manipulation, result visualization, database linkage, and cross-validation and comparison of rule sets, to complement the basic machine learning tools.
منابع مشابه
WEKA - Experiences with a Java Open-Source Project
WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.
متن کاملRegression model for Quality of Web Services dataset with WEKA
The Waikato Environment for Knowledge Analysis (WEKA) came about through the perceived need for a unified workbench that would allow researchers easy access to state-of the-art techniques in machine learning algorithms for data mining tasks. It provides a general-purpose environment for automatic classification, regression, clustering, and feature selection etc. in various research areas. This ...
متن کاملA Machine Learning Workbench for Data Mining
The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for design...
متن کاملManual for EAR4 and CAAR Weka Plugins
EAR4 and CAAR are lazy learners applying the case-based reasoning (CBR) paradigm to numerical prediction tasks. Both augment standard instance-based learning methods by applying automatically generated case adaptation rules to adjust solutions of prior cases, and both apply ensembles of the generated rules. CAAR augments the EAR approach with a richer treatment of case context, more context-awa...
متن کاملData mining in bioinformatics using Weka
UNLABELLED The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experiment...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1996