Belief Network (BN)
systems in the package: BN PowerPredictor, Data PreProcessor)
This system is based on my three-phase belief network
construction algorthims. It includes a wizard-like user interface and a
belief network construction engine. (A recent technical report about the
algorithms and the system is available for download: report98.ps.gz
32-bit windows systems on PC (Windows95/98, 2000 and NT).
2.2 Beta. (Update
History: Last update August 14, 2001)
- Input: A data set and optional domain knowledge (attribute
ordering, partial ordering, direct causes and effects -- initial
structure, forbidden links, root and leaf nodes). (Continuous values can
be detected and discretized.)
The belief network (including both structure and parameters) of the data
interface: (Click here to see a canned demo -- a group of screen
- Wizard-like interface. It
gathers necessary input information through 5 simple steps.
- Online help for each step.
- Graphical belief network
editor for modifying BN structure after the learning process.
- Accessibility. It supports
most of the popular desktop database and spreadsheet formats, including:
Ms-Access, dBase, Foxpro, Paradox, Excel and text file formats. It also
supports remote database servers like ORACLE, SQL-SERVER through ODBC.
- Reusability. The engine is
an ActiveX DLL(bnpcapi.dll), so it can be easily integrated into other
belief network, data mining or knowledge base systems for windows95/98/NT.
- Efficiency. This engine
constructs belief networks by using conditional independence(CI) tests. In
general, it requires CI tests to the complexity of O(N4); when
the attribute ordering is known, the complexity is O(N2). N is
the number of attributes (fields). To get a rough idea about the running
time, see the description
of some of my experiments. (In practice, the running time is about O(N2)
even without node ordering.)
- Reliability. Modified
conditional independence test method is used to make the results more
reliable when the data set is not large enough.
- Supporting domain
knowledge. Complete ordering, partial ordering, direct causes and effects,
forbidden links and root & leaf nodes can be used to constrain the
search space and therefore speed up the learning process.
- Supporting large data sets.
Running time is linear to the number of cases. See the
description of experiment 2.
- Reusability of CI test
results. A log file which contains CI test results of a particular data
set can be created. This information can speed up the construction process
greately when the same data set is being analyzed again with different
- Connectivity: The resulting
belief network can be exported to other belief network systems. (The
current version supports Hugin net format of Hugin Expert A/S, Netica format of Norsys Software Corp and Bayesian Interchange Format
-- BIF 0.15.)
- Supporting condensed data
sets, which has a 'frequency' fields that contains the number of
appearances of the current entry in the data.
What's new in Version 2
more types of domain knowledge, including: forbidden links, root &
leaf nodes. Direct causes & effects (initial structure) can now be
imported from several popular BN formats.
BN editor is added to the system. (Learning procedure: 1. select input data
set; 2. input domain knowledge; 3. start BN structure learning; 4. modify
the learned structure using BN editor; 5. start parameter (CP tables)
learning and export. )
system can now learn the parameters of BN as well.
system now support condensed data sets.
Download the package now! (including BN
PowerConstructor, BN PowerPredictor and Data PreProcessor)
Contact: Jie Cheng <email@example.com>
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