Code Description

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Code Reference

      The original version, documented version and adapted version of the code used can be found through the links below.

• Original code from Weka
• Documented code from Weka
• Adapted code

      The file and data handling code used in this project is from the Weka project (www.cs.waikato.ac.nz/ml/weka/).

      Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka is also well-suited for developing new machine learning schemes. Weka is open source software issued under the GNU public license.

      The Weka code used in this project was that which handles files and various data manipulation tasks. It was also used to collect statistics on the performance of the Naive Bayes Classifier algorithm implemented. The version of the Instance Based nearest k neighbor classifier was adapted from the Weka package Instance Based nearest neighbor (IB1) algorithm.

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      The Weka IB1 class implements a simple Instance Based nearest neighbor algorithm. The distance measure used for nominal (discrete) attributes is simply 0 if they are the same, 1 if they are different. For numeric attributes, the difference of their normalized values is used. If any attribute is missing, the distance used is 1.

      The classification of an instance was adapted to find the nearest k neighbors. The average class attribute value of these k nearest neighbors is rounded to the nearest class attribute value.

While Weka provides a more complicated IBk algorithm the IB1 algorithm was adapted and used for this project. If time allows, testing with the Weka supplied IBk will be done and compared.

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