Datasets and project suggestions:  Below are descriptions of several data sets, and some suggested projects.  The first few are spelled out in greater detail.  You are encouraged to select and flesh out one of these projects, or make up you own well-specified project using these datasets. To work on alternative datasets has to been approved by the instructors.
　

Summary

time series: A:fMRI F:bodymedia G:NBA

relational data: B:IMDB C:DBLP E:creditCard

text data: D:Newsgroup H:WebKB

---

A: Brain imaging data (fMRI)

This data set contains a time series of images of brain activation, measured using fMRI, with one image every 500 msec. During this time, human subjects performed 40 trials of a sentence-picture comparison task (reading a sentence, observing a picture, and determining whether the sentence correctly described the picture). Each of the 40 trials lasts approximately 30 seconds. Each image contains approximately 5,000 voxels (3D pixels), across a large portion of the brain. Data is available for 12 different human subjects. 
Available software: we can provide Matlab software for reading the data, manipulating and visualizing it, and for training some types of classifiers (Gassian Naive Bayes, SVM).


Project A1: Bayes network classifiers for fMRI
Project idea: Gaussian Naïve Bayes classifiers and SVMs have been used with this data to predict when the subject was reading a sentence versus perceiving a picture. Both of these classify 8-second windows of data into these two classes, achieving around 85% classification accuracy [Mitchell et al, 2004]. This project will explore going beyond the Gaussian Naïve Bayes classifier (which assumes voxel activities are conditionally independent), by training a Bayes network in particular a TAN tree [Friedman, et al., 1997]. Issues youll need to confront include which features to include (5000 voxels times 8 seconds of images is a lot of features) for classifier input, whether to train brain-specific or brain-independent classifiers, and a number of issues about efficient computation with this fairly large data set. Midpoint milestone: By Nov 8 you should have run at least one classification algorithm on this data and measured its accuracy using a cross validation test. This will put you in a good position to explore refinements of the algorithm, alternative feature encodings for the data, or competing algorithms, by the end of the semester. Project: Reducing dimensionality and classification accuracy.
Papers to read: "Learning to Decode Cognitive States from Brain Images," Mitchell et al., 2004, "Bayesian Network Classifiers" Friedman et al., 1997.

Project A2: Dimensionality reduction for fMRI data
Project idea:  Explore the use of dimensionality-reduction methods to improve classification accuracy with this data.  Given the extremely high dimension of the input (5000 voxels times 8 images) to the classifier, it is sensible to explore methods for reducing this to a small number of dimension. For example, consider PCA, hidden layers of neural nets, or other relevant dimensionality reducing methods.  PCA is an example of a method that finds lower dimension representations that minimize error in reconstructing the data.  In contract, neural network hidden layes are lower dimensional representations of the inputs that minimize classification error (but only find a local minimum).  Does one of these work better?  Does it depend on parameters such as the number of training examples?
Papers to read: "Learning to Decode Cognitive States from Brain Images," Mitchell et al., 2004, papers and textbook on PCA, neural nets, or whatever you propose to try.

Project A3: Feature selection/feature invention for fMRI classification.
Project idea:  As in many high dimensional data sets, automatic selection of a subset of features can have a strong positive impact on classifier accuracy.  We have found that selecting features by the difference in their activity when the subject performs the task, relative to their activity while the subject is resting, is one useful strategy [Mitchell et al., 2004].  In this project you could suggest, implement, and test alternative feature selection strategies (eg., consider  the incremental value of adding a new feature to the current feature set, instead of scoring each feature independent of other features that are being selected), and see whether you can obtain higher classification accuracies.   Alternatively, you could consider methods for synthesizing new features (e.g., define the 'smoothed value' of a voxel in terms of a spatial Gaussian kernel function applied to it and its neighbors, or define features by averaging voxels whose time series are highly correlated).
Papers to read: "Learning to Decode Cognitive States from Brain Images," Mitchell et al., 2004, papers on feature selection
　

　

---

B: IMDB Movie database and user rating data

This data set consists of two main parts:

• IMDB data: a movie database consists of many different attributes about movies. For example, movie title, genre, actors/actresses, directors, company, year, etc.

• User rating data: user ratings on different movies

Project ideas:

Project B1: Classification: To predict the user rating based on movie information. This involves feature selection and classification.

Project B2: Clustering evolution:  a lot of study on social network tries to identify the community structure in the social relation between among people. The most common structure is to cluster the people based on their interaction. A interesting study will be to model the changes of the clusters over time.

---

C: DBLP Research publication database

This data set consists of computer science bibliography data. The data is well-organized and can be downloaded at http://dblp.uni-trier.de/xml/. An interesting browser for viewing this dataset is available too.

Project ideas:

Project C1: clustering evolution: a lot of study on social network tries to identify the community structure in the social relation between among people. The most common structure is to cluster the people based on their interaction. A interesting study will be to model the changes of the clusters over time.

Project C2: distance measure study: a good distance function is crucial for the success of any learning algorithm. It is especially true for heterogeneous dataset like this, where naive distance function such as Euclidian distance is undefined.

---

D:Twenty Newgroups text data

This data set contains 1000 text articles posted to each of 20 online newgroups, for a total of 20,000 articles.  For documentation and download, see this website.  This data is useful for a variety of text classification and/or clustering projects.  The "label" of each article is which of the 20 newsgroups it belongs to.  The newsgroups (labels) are hierarchically organized (e.g., "sports", "hockey").

Available software: The same website provides an implementation of a Naive Bayes classifier for this text data.  The code is quite robust, and some documentation is available, but it is difficult code to modify.

Project ideas:
　

• EM for text classification in the case where you have labels for some documents, but not for others  (see McCallum et al, and come up with your own suggestions)
   

• Make up your own text learning problem/approach

---

E: Credit card data

This dataset comes from a real data mining competition recently (http://mill.ucsd.edu/).

When a company is evaluating whether an individual is a 'good' or 'bad' customer, it uses historical information from that customer's account.  For example, a credit card company might be interested in identifying customers that are likely to go bankrupt.  The company will use past transaction information to predict future bankruptcy.  Once a potentially 'bad' account is predicted, the company will take additional steps to verify the actual nature of the account.
Our goal is to develop a system that uses historical data and accurately predicts which accounts are likely to be 'bad.'
　

F: Physiological Data Modeling (bodymedia)

Physiological data offers many challenges to the machine learning community including dealing with large amounts of data, sequential data, issues of sensor fusion, and a rich domain complete with noise, hidden variables, and significant effects of context.