• 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.

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.

Please structure your written report and your oral presentation using the following outline, providing detailed descriptions (following the specific guidelines for this roject provided above as well as the general PROJECT GUIDELINES) of:

1. Dataset(s) used in this project.
2. Code used in this project.
3. Hypothesis encoding used (that is, how the machine learning model you chose is encoded as an individual/chromosome).
4. Size and initialization of the population.
5. Fitness function used.
6. Selection method used.
7. Cross-over method used.
8. Mutation method used.
9. Termination criteria used.
10. Domain-dependent guiding questions, experiments and analysis of results, as usual.
11. Advanced topic.