## ILLM Applications and Results

ILLM (Inductive Learning by Logic Minimization) is a set of codes designed to solve classification problems. Online rule induction system available through DMS is based on the algorithms from ILLM system. To illustrate the power of the actual ILLM methodology we have listed some real-world like data mining problems on which this methods were succesfully tested.

### KDD (Knowledge Discovery in Databases) Cup 1999

**Description**

Detailed description of the problem and results can be found __here__.

**Solution**

ILLM was used to produce rule-sets on random excerpts of few thousand samples. These were tested on the training set 10% of the size of total training data, and those scoring highest scores in terms of accuracy, were put in the pool for clasifying the test set. Final classification was performed using voting between different models, based on the cost sensitive confusion matrix.

**Score**

There were in total 24 solutions for in the contest. ILLM solution was 17th. KDD Cup 1999 stimulated us to work on new solutions in combining classifiers, and changes in the objective function of the ILLM rule-search algorithm.

### CoIL (Computational Intelligence and Learning) Challenge 2000

**Description**

__CoIL Challenge site__.

**Solution**

The challenge had two tasks which were evaluated separately. Since ILLM methodology is capable of giving efficient and informative classifiers in the form of conjunctions of literals, both the prediction and description tasks were tackled simultaneously. In the experimental phase we have used 5 fold cross validation on the training set to find out the "optimal" set of parameters for the ILLM rule-set search alogrithm, which would produce robust rule-sets with high lift. Final rule-set gave us high lift, but also an informative description of about half-a-dozen distinct customer subgroups.
Detailed description of the results and submissions can be obtained from the
__report__ prepared by P. van der Putten and M. van Someren.

**Score**

- Prediction task: 9th of 43
- Description task: 2nd of 43

### NIPS (Unlabeled Data Competition) 2000

**Description**

**Solution**

**Score**

### CINC (Computers in Cardiology) 2001

**Description**

**Solution**

**Score**

### PTC (Predictive Toxicology Challenge) 2001

**Description**

- generate reliable toxicity predictions for chemicals;
- enable low cost identification of hazardous chemicals; and
- refine and reduce the reliance on the use of large number of laboratory animals

**Solution**

- those produced by Dr. B. Lucic from Rudjer Boscovic Institute (DRAGON set);
- descriptor set produced by George Cowan of Pfizer;

**Score**

© 2001 LIS - Rudjer Boskovic Institute

Last modified: October 18 2018 16:54:31.