• Solve Exercises 9.1 and 9.4 of your textbook (page 270). Include your solutions in your written report (and not in your oral report).

Your written report should be at most 7 pages long (including all the graphs, figures, and appendices). The font size should be no smaller than 11pts.

• Data Mining Technique(s): Genetic Algorithms. For the genetics algorithm code, you can use one or more of the following alternatives:
  • write your own genetic algorithm code implementing selection, crossover, and mutation as they are described in Chapter 9 of the textbook.
  • Use the publicly available code at "http://lancet.mit.edu/ga/".
  • Use the GA code in WEKA used for attribute selection and extend it as necessary to produce a classifier that includes crossover, mutation, and selection operators as they are described in Chapter 9 of the textbook. See GeneticSearch.java under the attributeSelection. Alternatively, you can use the GA code in weka.classifiers.bayes.net.search.local.GeneticSearch (accessible from the Experimenter), which evolves a population of Bayesian Nets. If you wish, you can modify this code to evolve a population of a different type of models of your choice.
  • Matlab

• Dataset(s): In this project, we will use one dataset:

  • A dataset of your choice. This dataset can consist of data that you use for your own research or work, a dataset taken from a public data repository (e.g., UCI Machine Learning Repository, or from the UCI KDD Archive), or data that you collect from public data sources. This dataset should contain a large enough number of instances, and a combination of nominal and numeric attributes.

• Target Attribute: If possible, run a set of experiments using a nominal target attribute, and a separate set of experiments using a numeric target attribute.

• Hypothesis Encoding, Selection, Crossover, and Mutation: You can choose the strategy and parameters of your algorithm as you prefer. Section 9.3 provides a description of an approach to using genetic algorithms to construct a classifier. You can follow this approach or design your own if you wish.

  You should decide how to implement/represent the following notions/parameters in your system. Make sure you explain your design choices clearly in your written report as well as in your oral report (concisely):

  • individuals (i.e., what learning method you will train with GAs and how the hypotheses are encoded),
  • population,
  • fitness (in this part you should elaborate on how individuals are used to predict the target class of test data instances),
  • size of the population,
  • fitness threshold,
  • mutation rate,
  • fraction of population replaced by crossover at each step.

• Performance Metric(s):
  • Explain what metrics you use to evaluate the "goodness" of your models. Don't forget to look at the resulting hypothesis/model, and elaborate in your report on what the hypothesis "says".
  • Report the training time needed to construct the model in each of the experiments.