Remove Useless Attributes (AI Studio Core)

Synopsis

This operator removes useless attributes from an ExampleSet. The thresholds for useless attributes are specified by the user.

Description

The Remove Useless Attributes operator removes four kinds of useless attributes: Such nominal attributes where the most frequent value is contained in more than the specified ratio of all examples. The ratio is specified by the nominal useless above parameter. This ratio is defined as the number of examples with most frequent attribute value divided by the total number of examples. This property can be used for removing such nominal attributes where one value dominates all other values. Such nominal attributes where the most frequent value is contained in less than the specified ratio of all examples. The ratio is specified by the nominal useless below parameter. This ratio is defined as the number of examples with most frequent attribute value divided by the total number of examples. This property can be used for removing nominal attributes with too many possible values. Such numerical attributes where the Standard Deviation is less than or equal to a given deviation threshold. The numerical min deviation parameter specifies the deviation threshold. The Standard Deviation is a measure of how spread out values are. Standard Deviation is the square root of the Variance which is defined as the average of the squared differences from the Mean. Such nominal attributes where the value of all examples is unique. This property can be used to remove id-like attributes. Please note that this is not an intelligent operator i.e. it cannot figure out at its own whether an attribute is useless or not. It simply removes those attributes that satisfy the criteria for uselessness defined by the user.

Input

example set input (Data table)
This input port expects an ExampleSet. It is the output of the Filter Examples operator in the attached Example Process. The output of other operators can also be used as input.

Output

example set output (Data table)
The attributes that satisfy the user-defined criteria for useless attributes are removed from the ExampleSet and this ExampleSet is delivered through this output port.
original (Data table)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.

Parameters

numerical_min_deviationThe numerical min deviation parameter specifies the deviation threshold. Such numerical attributes where Standard Deviation is less than or equal to this deviation threshold are removed from the input ExampleSet. The Standard Deviation is a measure of how spread out values are. Standard Deviation is the square root of the Variance which is defined as the average of the squared differences from the Mean. Range: real
nominal_useless_aboveThe nominal useless above parameter specifies the ratio of the number of examples with most frequent value to the total number of examples. Such nominal attributes where the ratio of the number of examples with most frequent value to the total number of examples is more than this ratio are removed from the input ExampleSet. This property can be used to remove such nominal attributes where one value dominates all other values. Range: real
nominal_remove_id_likeIf this parameter is set to true, all such nominal attributes where the value of all examples is unique are removed from the input ExampleSet. This property can be used to remove id-like attributes. Range: boolean
nominal_useless_belowThe nominal useless below parameter specifies the ratio of the number of examples with most frequent value to the total number of examples. Such nominal attributes where the ratio of the number of examples with most frequent value to the total number of examples is less than this ratio are removed from the input ExampleSet. This property can be used to remove nominal attributes with too many possible values. Range: real

Tutorial Processes

Removing useless nominal attributes from an ExampleSet

This Example Process explains how the nominal useless above and nominal useless below parameters can be used to remove useless nominal attributes. Please keep in mind that the Remove Useless Attributes operator removes those attributes that satisfy the user-defined criteria for useless attributes.

The 'Golf' data set is loaded using the Retrieve operator. The Filter Examples operator is applied on it to filter the first 10 examples. This is done to just simplify the calculations for understanding this process. A breakpoint is inserted after the Filter Examples operator so that you can see the ExampleSet before application of the Remove Useless Attributes operator. You can see that the ExampleSet has 10 examples. There are 2 regular nominal attributes: 'Outlook' and 'Wind'. The most frequent values in the 'Outlook' attribute are 'rain' and 'sunny', they occur in 4 out of 10 examples. Thus their ratio is 0.4. The most frequent value in the 'Wind' attribute is 'false', it occurs in 7 out of 10 examples. Thus its ratio is 0.7.

The Remove Useless Attributes operator is applied on the ExampleSet. The nominal useless above parameter is set to 0.6. Thus attributes where the ratio of most frequent value to total number of examples is above 0.6 are removed from the ExampleSet. As the ratio of most frequent value in the Wind attribute is greater than 0.6, it is removed from the ExampleSet.

The nominal useless below parameter is set to 0.5. Thus attributes where the ratio of most frequent value to total number of examples is below 0.5 are removed from the ExampleSet. As the ratio of most frequent value in the Outlook attribute is below 0.5, it is removed from the ExampleSet.

This can be verified by seeing the results in the Results Workspace.

Removing useless numerical attributes from an ExampleSet

This Example Process explains how the numerical min deviation parameter can be used to remove useless numerical attributes. The numerical min deviation parameter specifies the deviation threshold. Such numerical attributes where the Standard Deviation is less than or equal to this deviation threshold are removed from the input ExampleSet. The Standard Deviation is a measure of how spread out values are. Standard Deviation is the square root of the Variance which is defined as the average of the squared differences from the Mean. Please keep in mind that the Remove Useless Attributes operator removes those attributes that satisfy the user-defined criteria for useless attributes.

The 'Golf' data set is loaded using the Retrieve operator. The Filter Examples operator is applied on it to filter the first 10 examples. This is done to just simplify the calculations for understanding this process. A breakpoint is inserted after the Filter Examples operator so that you see the ExampleSet before application of the Remove Useless Attributes operator. You can see that it has 10 examples. There are 2 regular numerical attributes: 'Temperature' and 'Humidity'. The Aggregate operator is applied on the ExampleSet to calculate and display the Standard Deviations of both numerical attributes. This operator is inserted here so that you can see that Standard Deviations without actually calculating them, otherwise this operator is not required here. You can see that the Standard Deviation of the 'Temperature' and 'Humidity' attributes is 7.400 and 10.682 respectively.

The Remove Useless Attributes operator is applied on the original ExampleSet (the ExampleSet with the first 10 examples of the 'Golf' data set). The numerical min deviation parameter is set to 9.0. Thus the numerical attributes where the Standard Deviation is less than 9.0 are removed from the ExampleSet. As the Standard Deviation of the Temperature attribute is less than 9.0, it is removed from the ExampleSet.