Converts nominal data into a series of binary encoded columns for input to a neural network. It also reverses the aforementioned encoding, accepting binary encoded data and returns an array of integers representing the classes for a nominal variable.

Namespace: Imsl.DataMining.Neural
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.0.0

Syntax

C#
[SerializableAttribute]
public class UnsupervisedNominalFilter
Visual Basic (Declaration)
<SerializableAttribute> _
Public Class UnsupervisedNominalFilter
Visual C++
[SerializableAttribute]
public ref class UnsupervisedNominalFilter

Remarks

Binary Encoding

Method Encode can be used to apply binary encoding. Referring to the result as z, binary encoding takes each category in the nominal variable x, and creates a column in z containing all zeros and ones. A value of zero indicates that this category was not present and a value of one indicates that it is present.

For example, if x[]={2,1,3,4,2,4} then nClasses=4, and

z =\begin{array}{rrrr}
            0 & 1 & 0 & 0 \\
            1 & 0 & 0 & 0 \\
            0 & 0 & 1 & 0 \\
            0 & 0 & 0 & 1 \\
            0 & 1 & 0 & 0 \\
            0 & 0 & 0 & 1 \\
            \end{array}
Notice that the number of columns in the result, z, is equal to the number of distinct classes in x. The number of rows in z is equal to the length of x.

Binary Decoding

Unfiltering can be performed using the method Decode. In this case, z is the input, and we refer to x as the output. Binary unfiltering takes binary representation in z, and returns the appropriate class in x.

For example, if a row in z equals {0, 1, 0, 0}, then the return value from Decode would be 2 for that row. If a row in z equals {1,0,0,0}, then the return value from Decode would be 1 for that row. Notice these are the same values as the first two elements of the original x because classes are numbered sequentially from 1 to nClasses. This ensures that the results of Decode are associated with the i-th class in x.

Inheritance Hierarchy

System..::.Object
Imsl.DataMining.Neural..::.UnsupervisedNominalFilter

See Also