An implementation of impact rules can be downloaded here.
We have contributed numerous components to the Weka machine learning workbench. These include:
- AnDE: averaged n-dependence estimators, an efficient technique for relaxing the attribute-independence assumption of naive Bayes. [papers]
- AODE: averaged one-dependence estimators, AnDE with n=1. [papers]
- AODEsr: AODE with subsumption resolution. [papers]
- BVDecomposeSegCVSub: Bias-variance decomposition using the sub-sampled cross-validation procedure. [paper]
- J48Graft: adds grafting to J48. [ papers ]
- LBR: lazy Bayesian rules, a lazy learning approach to lessening the attribute-independence assumption of naive Bayes. [papers]
- MultiBoostAB: an ensemble learning technique that combines boosting and bagging, attaining much of the former's superior bias reduction together with much of the latter's superior variance reduction. [papers]
- PKIDiscretize: proportional k-interval discretization, a discretization technique for naive Bayes. [papers]
- WANBIA: a system that uses naive Bayes to precondition logistic regression [ papers ]
ALR is an open source implementation of our big models for big data learning algorithm.
EBNC implements our algorithms for Efficient Parameter Learning of Bayesian Networks.
Softmax Logistic Regression (for both Continuous and Discrete data) - with TRON + QuasiNewton + Conjugate Gradient optimisations.
The Knowledge Factory is an expert system development environment that incorporates interactive rule induction. The Knowledge Factory works with you to produce and refine expert systems.
Our software for generating synthetic data streams with abrupt drift can be downloaded here.
Our system for describing the concept drift present in real-world data can be downloaded here.
One of my MDS students, Jieshen Huang, implemented impact rules in Python. The source can be found here.