605.449 — Introduction to Machine Learning Programming Project #6 Solved

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Description

The purpose of this assignment is to give you experience developing one of the main algorithms for training feedforward neural networks—backpropagation. In this project, you will evaluate the performance of a feedforward network trained with backprop. For extra credit, you will compare the feedforward network to your previous implementation of the radial basis function network. If you did not implement the RBF network previous, you can do so now. You will also use the same five datasets that you used from Project 1 and Project 4 from the UCI Machine Learning Repository, namely:

1. 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.

  1. Glass — https://archive.ics.uci.edu/ml/datasets/Glass+Identification

The study of classification of types of glass was motivated by criminological investigation.

3. 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.

  1. Soybean (small) — https://archive.ics.uci.edu/ml/datasets/Soybean+%28Small%29 A small subset of the original soybean database.

5. 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.

  1. Some of the data sets have missing attribute values. When this occurs in low numbers, you may simplyedit 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.
  2. 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. You should apply what is called “one-hot coding” where you create a separate Boolean attribute for each value. For the continuous attributes, you may use one-hot-coding if you wish, but there is actually a better way. Specifically, it is recommended that you normalize them first to be in the range −1 to +1. (If you want to normalize to be in the range 0 to 1, that’s fine. Just be consistent.)

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).
  • Implement backpropagation for training feedforward neural networks. You may choose whether or not you wish to use the logistic activation function or the hyperbolic tangent activation function. Remember that this choice affects the update rules because of having different derivatives. It is also your choice whether or not you use momentum.

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  • Run your algorithms on each of the data sets. These runs should be done with 5-fold cross-validation so you can compare your results statistically. You can use classification error or mean squared error (as appropriate) for your loss function.
  • Run your feedforward network trained with backpropagation on each of the data sets. These runs should output the classifications on all of the test examples. If you are doing cross-validation, just output classifications for one fold each. Run your experiments with no hidden layers, one hidden layer, and two hidden layers. It is up to you to tune the number of hidden nodes per layer, and be sure to explain how you did the tuning.
  • Extra Credit (for up to 20 additional points): Run your previous implementation of the RBF network (implement here if you did not do it previously) and compare the results to the feedforward network.
  • Write a very brief paper that incorporates the following elements, summarizing the results of your experiments. You should also output the summary statistics on classification accuracy.
    1. Title and author name
    2. A brief, one paragraph abstract summarizing the results of the experiments
    3. Problem statement, including hypothesis, projecting how you expect each algorithm to perform
    4. Brief description of algorithms implemented
    5. Brief description of your experimental approach
    6. Presentation of the results of your experiments
    7. A discussion of the behavior of your algorithms, combined with any conclusions you can draw
    8. Summary
    9. References (you should have at least one reference related to each of the algorithms implemented,a reference to the data sources, and any other references you consider to be relevant)
  • Submit your fully documented code, the outputs from running your programs, and your paper. Your grade will be broken down as follows:
    • Code structure – 10%
    • Code documentation/commenting – 10%
    • Proper functioning of your code, as illustrated by a 5 minute video – 30%
    • Summary paper – 50%

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