A1DE (3.7)

AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes

The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks.

For more information, see

G.Webb, J.

Boughton, Z.Wang (2005).

Not So Naive Bayes: Aggregating One-Dependence Estimators.Machine Learning.


Further papers are available athttp://www.csse.monash.edu.au/~webb/.

Use m-estimate for smoothing base probability estimates witha default of 1 (m value can changed via option -M).

Default mode is non-incremental that is probabilites are computed at learning time.An incremental version can be used via option -I.

Default frequency limit set to 1.

Subsumption Resolution can be achieved by using -S option.Weighting of SPODE can be done by using -W option.

Weights are calculated based on mutual information between attribute and the class.The weighting scheme is developed by L.

Jiang and H.Zhang

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


A1DE Options

D: Output debugging information

F: Impose a frequency limit for superParents (default is 1)

M: Specify a weight to use with m-estimate (default is 1)

S: Specify a critical value for specialization-generalilzation SR (default is 100)

W: Specify if to use weighted AODE

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [] min # Instance: 0

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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