Weka
Weka (Waikato Environment for Knowledge Analysis) is a
comprehensive popular
suite of machine learning software written in Java, developed at the
University of Waikato, New Zealand. It is a collection of
machine learning algorithms for solving real-world data mining
problems including decision trees, support vector machines,
instance-based classifiers, Bayes decision schemes, neural networks
etc. and clustering. The algorithms can either be applied directly to a
dataset or called from your own Java code.
The Weka workbench contains a collection of visualization
tools and algorithms for data analysis and predictive modeling,
together with graphical user interfaces for easy access to this
functionality.
Features include:
- Provides an environment with algorithms for data
preprocessing, feature selection, classification, regression, and
clustering
- Graphical user interface that makes applying machine
learning algorithms easy
- Four graphical user interface modules:
- Explorer
- Experimenter
- Knowledge Flow
- Simple Command Line Interface
- Schemes for classification include:
- Decision
trees, rule learners, naive Bayes, decision tables, locally weighted
regression, SVMs, instance-based learners, logistic regression, voted
perceptrons, multi-layer perceptron
- Schemes for numeric prediction include:
- Lnear
regression, model tree generators, locally weighted regression,
instance-based learners, decision tables, multi-layer perceptron
- Meta-schemes include:
- Bagging, boosting, stacking, regression via
classification, classification via regression, cost sensitive
classification
- Schemes for clustering:
- Schemes for feature selection
- Provides implementations of learning algorithms:
- Classification
- Clustering
- Association Rule Mining
- Attribute Selection
- General API to embed WEKA in other applications
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Last Updated Monday, April 09 2012 @ 04:46 AM EDT
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