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Introduction to Weka & Data Preprocessing

Introduction to Weka & Data Preprocessing | Information Technology

Introduction to Weka & Data Preprocessing 1
The WEKA GUI Chooser window is used to launch WEKA’s graphical environments. WEKA Explorer is an environment for exploring data with WEKA. In this lab, we will be focusing on creating an ARFF file and reading it into WEKA, and using the WEKA Explorer.

Creating an ARFF file

Attribute-Relation File Format (ARFF) is a file format recognized by WEKA. An ARFF file typically has a .arff extension and contains two sections – a Header section and a Data section. A separate file named ARFF.txt explaining the ARFF specifications has been uploaded herewith. You are required to go through the file and understand the specifications, before you proceed further.
Now follow the steps given below to create an ARFF file.
Copy the data given in the file assignment4partI.txt, to an Excel sheet.
Save the data set as CSV format.
Open it with a word processor and format it according to the ARFF specifications. Save as assignment4.arff.
Note: Please refer to the manual to create a proper .arff file.

The Weka Explorer

Section Tabs
At the very top of the window, just below the title bar, is a row of tabs. When the Explorer is first started only the first tab is active. The tabs are as follows: Pre-process, Classify, Cluster, Associate, Select Attributes, and Visualize.

Status Box

The status box appears at the very bottom of the window. It displays messages that keep you informed about what’s going on.

Opening files

The first button at the top of the preprocess section Open File enables us to load data into WEKA. Clicking that button brings up a dialogue box allowing you to browse for the data file on the local file system. Using the Open File button, read in the ARFF file you already created in this lab.

The Current Relation

Once the data has been loaded, the Preprocess panel shows a variety of information. The Current Relation box displays three entities – the name of the relation, the number of attributes in the data, and the number of instances in the data.

Below the Current Relation box is a box titled Attributes. There are three buttons and beneath them is a list of attributes in the current relation. The three buttons – AllNone, and Invert can be used to select desired attributes from the list.
When you click on different rows in the list of attributes, the fields change in the box to the right titled Selected Attribute. This box displays the characteristics of the currently highlighted attribute, namely – Name, Type, Missing, Distinct, and Unique.
Below these is a list showing more information about the values stored in this attribute, which differ depending on its type.  For instance, if the attribute is numeric, the list gives four statistics describing the distribution of value in the data – the minimum, maximum, mean, and standard deviation. And below these is a colored histogram, color-coded according to the attribute chosen as the Class using the box above the histogram. Note that only nominal Classattributes will result in a color-coding. After pressing the Visualize All button, histograms for all the attributes are shown in a separate window
Desired attributes can be removed by using the Remove button below the list of attributes. This can be undone by clicking the Undo button which is located in the top-right corner of the Preprocess panel. The Edit button next to it can be used to modify your data manually in a dataset editor.
You are expected to explore, observe and understand the purpose of each button under the preprocess panel after loading the ARFF file you prepared in this lab. Also, try to interpret what you observe using a different ARFF file, weather.arff, provided with WEKA.

Presentation of findings:

Please submit your assignment4.arff file.
Part II – Data Preprocessing
Objective: Understanding the purpose of unsupervised attribute/instance filters for preprocessing the input data.
Open the file breast_cancerpp.arff provided to you and carry out the following preprocessing tasks.
Follow the steps mentioned below to configure and apply a filter.
The preprocess section allows filters to be defined that transform the data in various ways. The Filter box is used to set up filters that are required. At the left of the Filter box is a Choose button. By clicking this button it is possible to select one of the filters in Weka. Once a filter has been selected, its name and options are shown in the field next to the Choose button. Clicking on this box brings up a GenericObjectEditor dialog box, which lets you configure a filter. Once you are happy with the settings you have chosen, click OK to return to the main Explorer window.
Now you can apply it to the data by pressing the Apply button at the right end of the Filter panel. The Preprocess panel will then show the transformed data. The change can be undone using the Undo button. Use the Edit button to view your transformed data in the dataset editor.
Try each of the following Unsupervised Attribute Filters.
(Choose -> weka -> filters -> unsupervised -> attribute)
  • Use ReplaceMissingValues to replace missing values in the given dataset.
  • Use the filter Add to add the attribute Average. Find the average of one attribute
  • Use the filter AddExpression and add an attribute which is the average of two columns. Name this attribute as
  • Understand the purpose of the attribute filter
  • Perform Normalize and Standardize on the dataset and identify the difference between these operations.
  • Add a nominal attribute Grade and use the filter MakeIndicator to convert the attribute into a Boolean attribute.
  • Try if you can accomplish the task in the previous step using the filter MergeTwoValues.
  • Try the following transformation functions and identify the purpose of each
        • NumericTransform
        • NominalToBinary
        • NumericToBinary            
        • Remove
        • RemoveType
        • RemoveUseless
        • ReplaceMissingValues
        • SwapValues
Try the following Unsupervised Instance Filters. (use a different data set if necessary)
(Choose -> weka -> filters -> unsupervised -> instance)
  • Perform Randomize on the given dataset and try to correlate the resultant sequence with the given one.
  • Use RemoveRange filter to remove the last two instances.
  • Use RemovePercent to remove 10 percent of the dataset.
  • Apply the filter RemoveWithValues to a nominal and a numeric attribute
Presentation of findings:
Please make a presentation of what you have learned from this activity. Please include pictures (screen shots of findings) and minimal text to make your point.
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