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A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both. The network can also be monitored and modified during training time. The nodes in this network are all sigmoid (except for when the class is numeric in which case the the output nodes become unthresholded linear units).


The table below describes the options available for MultilayerPerceptron.




Brings up a gui interface. This will allow the pausing and altering of the nueral network during training.

  • To add a node left click (this node will be automatically selected, ensure no other nodes were selected).
  • To select a node left click on it either while no other node is selected or while holding down the control key (this toggles that node as being selected and not selected.
  • To connect a node, first have the start node(s) selected, then click either the end node or on an empty space (this will create a new node that is connected with the selected nodes). The selection status of nodes will stay the same after the connection. (Note these are directed connections, also a connection between two nodes will not be established more than once and certain connections that are deemed to be invalid will not be made).
  • To remove a connection select one of the connected node(s) in the connection and then right click the other node (it does not matter whether the node is the start or end the connection will be removed).
  • To remove a node right click it while no other nodes (including it) are selected. (This will also remove all connections to it)
  • To deselect a node either left click it while holding down control, or right click on empty space.
  • The raw inputs are provided from the labels on the left.
  • The red nodes are hidden layers.
  • The orange nodes are the output nodes.
  • The labels on the right show the class the output node represents. Note that with a numeric class the output node will automatically be made into an unthresholded linear unit.

    Alterations to the neural network can only be done while the network is not running, This also applies to the learning rate and other fields on the control panel.

  • You can accept the network as being finished at any time.
  • The network is automatically paused at the beginning.
  • There is a running indication of what epoch the network is up to and what the (rough) error for that epoch was (or for the validation if that is being used). Note that this error value is based on a network that changes as the value is computed. (also depending on whether the class is normalized will effect the error reported for numeric classes.
  • Once the network is done it will pause again and either wait to be accepted or trained more.

    Note that if the gui is not set the network will not require any interaction.


Adds and connects up hidden layers in the network.


If set to true, classifier may output additional info to the console.


This will cause the learning rate to decrease. This will divide the starting learning rate by the epoch number, to determine what the current learning rate should be. This may help to stop the network from diverging from the target output, as well as improve general performance. Note that the decaying learning rate will not be shown in the gui, only the original learning rate. If the learning rate is changed in the gui, this is treated as the starting learning rate.


This defines the hidden layers of the neural network. This is a list of positive whole numbers. 1 for each hidden layer. Comma seperated. To have no hidden layers put a single 0 here. This will only be used if autobuild is set. There are also wildcard values 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' = classes , 't' = attribs + classes.


The amount the weights are updated.


Momentum applied to the weights during updating.


This will preprocess the instances with the filter. This could help improve performance if there are nominal attributes in the data.


This will normalize the attributes. This could help improve performance of the network. This is not reliant on the class being numeric. This will also normalize nominal attributes as well (after they have been run through the nominal to binary filter if that is in use) so that the nominal values are between -1 and 1


This will normalize the class if it's numeric. This could help improve performance of the network, It normalizes the class to be between -1 and 1. Note that this is only internally, the output will be scaled back to the original range.


Seed used to initialise the random number generator.Random numbers are used for setting the initial weights of the connections betweem nodes, and also for shuffling the training data.


This will allow the network to reset with a lower learning rate. If the network diverges from the answer this will automatically reset the network with a lower learning rate and begin training again. This option is only available if the gui is not set. Note that if the network diverges but isn't allowed to reset it will fail the training process and return an error message.


The number of epochs to train through. If the validation set is non-zero then it can terminate the network early


The percentage size of the validation set.(The training will continue until it is observed that the error on the validation set has been consistently getting worse, or if the training time is reached).
If This is set to zero no validation set will be used and instead the network will train for the specified number of epochs.


Used to terminate validation testing.The value here dictates how many times in a row the validation set error can get worse before training is terminated.


The table below describes the capabilites of MultilayerPerceptron.




Nominal class, Binary class, Missing class values, Date class, Numeric class


Empty nominal attributes, Numeric attributes, Binary attributes, Missing values, Date attributes, Unary attributes, Nominal attributes

Min # of instances


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