Description
Breast Cancer — https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
This breast cancer databases was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.
Glass — https://archive.ics.uci.edu/ml/datasets/Glass+Identification
The study of classification of types of glass was motivated by criminological investigation.
Iris — https://archive.ics.uci.edu/ml/datasets/Iris
The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
Soybean (small) — https://archive.ics.uci.edu/ml/datasets/Soybean+%28Small%29
A small subset of the original soybean database.
Vote — https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records
This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the Congressional Quarterly Almanac.
When using these data sets, be careful of some issues.
Not all of these data sets correspond to 2-class classification problems. For Naïve Bayes, that is not really problem. But for Winnow-2, you will need to use one classifier for each class. To be fair in comparing the two, you may want to generate Boolean classifiers for Naïve Bayes, even though this is not strictly required.
Some of the data sets have missing attribute values. When this occurs in low numbers, you may simply edit the corresponding values out of the data sets. For more occurrences, you should do some kind of “data imputation” where, basically, you generate a value of some kind. This can be purely random, or it can be sampled according to the conditional probability of the values occurring, given the underlying class for that example. The choice is yours, but be sure to document your choice.
Most of attributes in the various data sets are either multi-value discrete (categorical) or real-valued. You will need to deal with this in some way. For the multi-value situation, once again Naïve Bayes should be fine, but Winnow-2 will have a problem. In that case, you can apply what is called “onehot coding” where you create a separate Boolean attribute for each value. Again, I recommend you go ahead and use this for Naïve Bayes, even though it is not really necessary. For the continuous attributes, you will need to discretize them in some way for both algorithms and then proceed as in the multi-valued categorical case.
For this project, the following steps are required:
• Download the five (5) data sets from the UCI Machine Learning repository. You can find this repository at http://archive.ics.uci.edu/ml/. All of the specific URLs are also provided above.
• Pre-process each data set as necessary to handle missing data and non-Boolean data (both classes and attributes).
• Set up your test and training sets from the provided data. Specifically, split the data into two groups randomly where 2/3 of the data will be used for training and 1/3 will be used for testing. If you are more ambitious, you may set up a cross-validation experiment. In that case, I recommend either 10-fold cross-validation or 5 × 2 cross-validation. If you don’t know what this means, don’t worry about it for now.
• Implement both Naïve Bayes and Winnow-2.
• Run your algorithms on each of the data sets. These runs should output the learned models in a way that can be interpreted by a human, and they should output the classifications on all of the test examples. If you are doing cross-validation, just output classifications for one fold each.
