KDD Cup 2010: Educational Data Mining Challenge

Feature engineering and classifier ensembling for KDD CUP 2010

Team Leader

Hsiang-Fu Yu
National Taiwan University
Taiwan

Team Members

Chih-Jen Lin
National Taiwan University

Hsuan-Tien Lin
National Taiwan University

Shou-De Lin
National Taiwan University

Yin-Hsuan Wei
National Taiwan University

Jui-Yu Weng
National Taiwan University

Chun-Fu Chang
National Taiwan University

En-Syu Yan
National Taiwan University

Todd McKenzie
National Taiwan University

Jing-Kai Lou
National Taiwan University

Hsun-Ping Hsieh
National Taiwan University

Jung-Wei Chou
National Taiwan University

Chia-Hua Ho
National Taiwan University

Po-Han Chung
National Taiwan University

Hung-Yi Lo
National Taiwan University

Che-Wei Chang
National Taiwan University

Tsung-Ting Kuo
National Taiwan University

Yi-Chen Lo
National Taiwan University

Chieh Po
National Taiwan University

Chien-Yuan Wang
National Taiwan University

Po Tzu Chang
National Taiwan University

Yu-Shi Lin
National Taiwan University

Yi-Hung Huang
National Taiwan University

Chen-Wei Hung
National Taiwan University

Yu-Xun Ruan
National Taiwan University

Overview
Method
Results
References

Overview

Supplementary online material

Provide a URL to a web page, technical memorandum, or a paper.

Background*

Provide a general summary with relevant background information: Where does the method come from? Is it novel? Name the prior art.

At National Taiwan University, we design a course targeting at KDD CUP 2010. 19 students and one non-registered RA were split to seven groups. Six groups expand features by various binarization and discretization techniques. The resulting sparse feature sets are trained by logistic regression (using LIBLINEAR). One group condenses features so that the number is less than 20. Then random forest is applied (using Weka). Initial development was conducted on an internal split of training data for training and validation. We identify some useful feature combination. For the final submission, each group submits a few results and TAs ensemble them by linear regression.

Method

Summarize the algorithms you used in a way that those skilled in the art should understand what to do. Profile of your methods as follows:

Data exploration and understanding

Did you use data exploration techniques to

Identify selection biases
Identify temporal effects (e.g. students getting better over time)
Understand the variables
Explore the usefulness of the KC models
Understand the relationships between the different KC types

Please describe your data understanding efforts, and interesting observations:

No response.

Preprocessing

Feature generation

Features designed to capture the step type (e.g. enter given, or ... )
Features based on the textual step name
Features designed to capture the KC type
Features based on the textual KC name
Features derived from opportunity counts
Features derived from the problem name
Features based on student ID
Other features

Details on feature generation:

Features derived from the step name Features based on unit ID Features based on section ID

Feature selection

Feature ranking with correlation or other criterion (specify below)
Filter method (other than feature ranking)
Wrapper with forward or backward selection (nested subset method)
Wrapper with intensive search (subsets not nested)
Embedded method
Other method not listed above (specify below)

Details on feature selection:

No response.

Did you attempt to identify latent factors?

Cluster students
Cluster knowledge components
Cluster steps
Latent feature discovery was performed jointly with learning

Details on latent factor discovery (techniques used, useful student/step features, how were the factors used, etc.):

No response.

Other preprocessing

Filling missing values (for KC)
Principal component analysis

More details on preprocessing:

No response.

Classification

Base classifier

Decision tree, stub, or Random Forest
Linear classifier (Fisher's discriminant, SVM, linear regression)
Non-linear kernel method (SVM, kernel ridge regression, kernel logistic regression)
Naïve
Bayesian Network (other than Naïve Bayes)
Neural Network
Bayesian Neural Network
Nearest neighbors
Latent variable models (e.g. matrix factorization)
Neighborhood/correlation based collaborative filtering
Bayesian Knowledge Tracing
Additive Factor Model
Item Response Theory
Other classifier not listed above (specify below)

Loss Function

Hinge loss (like in SVM)
Square loss (like in ridge regression)
Logistic loss or cross-entropy (like in logistic regression)
Exponential loss (like in boosting)
None
Don't know
Other loss (specify below)

Regularizer

One-norm (sum of weight magnitudes, like in Lasso)
Two-norm (||w||^2, like in ridge regression and regular SVM)
Structured regularizer (like in group lasso)
None
Don't know
Other (specify below)

Ensemble Method

Boosting
Bagging (check this if you use Random Forest)
Other ensemble method
None

Were you able to use information present only in the training set?

Corrects, incorrects, hints
Step start/end times

Did you use post-training calibration to obtain accurate probabilities?

Did you make use of the development data sets for training?

Details on classification:

No response.

Model selection/hyperparameter selection

We used the online feedback of the leaderboard.
K-fold or leave-one-out cross-validation (using training data)
Virtual leave-one-out (closed for estimations of LOO with a single classifier training)
Out-of-bag estimation (for bagging methods)
Bootstrap estimation (other than out-of-bag)
Other cross-validation method
Bayesian model selection
Penalty-based method (non-Bayesian)
Bi-level optimization
Other method not listed above (specify below)

Details on model selection:

No response.

Results

Final Team Submission

Scores shown in the table below are Cup scores, not leaderboard scores. The difference between the two is described on the Evaluation page.

A reader should also know from reading the fact sheet what the strength of the method is.

Please comment about the following:

Quantitative advantages (e.g., compact feature subset, simplicity, computational advantages).

- Sparse feature set - Fast linear classifier

Qualitative advantages (e.g. compute posterior probabilities, theoretically motivated, has some elements of novelty).

- Effective ensemble of classifiers - Posterior probabilities from logistic regression models

Other methods. List other methods you tried.

No response.

How helpful did you find the included KC models?

Crucial in getting good predictions
Somewhat helpful in getting good predictions
Neutral
Not particularly helpful
Irrelevant

If you learned latent factors, how helpful were they?

Crucial in getting good predictions
Somewhat helpful in getting good predictions
Neutral
Not particularly helpful
Irrelevant

Details on the relevance of the KC models and latent factors:

No response.

Software Implementation

Availability

Proprietary in-house software
Commercially available in-house software
Freeware or shareware in-house software
Off-the-shelf third party commercial software
Off-the-shelf third party freeware or shareware

Language

C/C++
Java
Matlab
Python/NumPy/SciPy
Other (specify below)

Details on software implementation:

We use two packages to conduct classification - LIBLINEAR http://www.csie.ntu.edu.tw/~cjlin/liblinear/ - Weka http://www.cs.waikato.ac.nz/ml/weka We use linear regression for ensembling classifiers.

Hardware implementation

Platform

Windows
Linux or other Unix
Mac OS
Other (specify below)

Memory

<= 2 GB
<= 8 GB
>= 8 GB
>= 32 GB

Parallelism

Multi-processor machine
Run in parallel different algorithms on different machines
Other (specify below)

Details on hardware implementation. Specify whether you provide a self contained-application or libraries.

No response.

Code URL

Provide a URL for the code (if available):

No response.

Competition Setup

From a performance point of view, the training set was

Too big (could have achieved the same performance with significantly less data)
Too small (more data would have led to better performance)

From a computational point of view, the training set was

Too big (imposed serious computational challenges, limited the types of methods that can be applied)
Adequate (the computational load was easy to handle)

Was the time constraint imposed by the challenge a difficulty or did you feel enough time to understand the data, prepare it, and train models?

Not enough time
Enough time
It was enough time to do something decent, but there was a lot left to explore. With more time performance could have been significantly improved.

How likely are you to keep working on this problem?

It is my main research area.
It was a very interesting problem. I'll keep working on it.
This data is a good fit for the data mining methods I am using/developing. I will use it in the future for empirical evaluation.
Maybe I'll try some ideas , but it is not high priority.
Not likely to keep working on it.

Comments on the problem (What aspects of the problem you found most interesting? Did it inspire you to develop new techniques?)

No response.

References

List references below.

No response.

KDD Cup 2010: Educational Data Mining Challenge

Sponsored by the Pittsburgh Science of Learning Center

Results > National Taiwan University

Feature engineering and classifier ensembling for KDD CUP 2010

Team Leader

Team Members

Overview

Supplementary online material

Background*

Method

Data exploration and understanding

Did you use data exploration techniques to

Preprocessing

Feature generation

Feature selection

Did you attempt to identify latent factors?

Other preprocessing

Classification

Base classifier

Loss Function

Regularizer

Ensemble Method

Were you able to use information present only in the training set?

Did you use post-training calibration to obtain accurate probabilities?

Did you make use of the development data sets for training?

Model selection/hyperparameter selection

Results

Final Team Submission

Quantitative advantages (e.g., compact feature subset, simplicity, computational advantages).

Qualitative advantages (e.g. compute posterior probabilities, theoretically motivated, has some elements of novelty).

Other methods. List other methods you tried.

How helpful did you find the included KC models?

If you learned latent factors, how helpful were they?

Software Implementation

Availability

Language

Hardware implementation

Platform

Memory

Parallelism

Code URL

Competition Setup

From a performance point of view, the training set was

From a computational point of view, the training set was

Was the time constraint imposed by the challenge a difficulty or did you feel enough time to understand the data, prepare it, and train models?

How likely are you to keep working on this problem?

Comments on the problem (What aspects of the problem you found most interesting? Did it inspire you to develop new techniques?)

References

Facebook

Elsevier

ACM

Carnegie Learning

IBM Research

DataShop