4 months, 2 weeks ago

Second semester project for Decision Systems course 2023/2024

This is the second project for students enrolled in the Decision System 2023/2024 course at the Faculty of Mathematics, Informatics, and Mechanics at the University of Warsaw.

Overview

The goal of this competition is to find informative subsets of genes that allow to efficiently solve classification problems defined for a number of microarray data sets.

More detailed competition rules are given in the Terms and Conditions.

The description of the data and evaluation metric is in the Task description section. 

The deadline for sending submissions and scores is January 27, 2024.

Terms & Conditions
 
 
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Rank Team Name Is Report Preliminary Score Final Score Submissions
1
Zespol123
True 0.7038 0.662100 27
2
Szwed
True 0.6960 0.657200 1
3
Szymon
True 0.7131 0.656700 5
4
Chemex
True 0.6958 0.647400 15
5
baseline
True 0.6284 0.646400 3
6
accuracy2.137
True 0.6952 0.642600 13
7
kczar
True 0.7084 0.636100 8
8
abc
True 0.6193 0.629300 14
9
nomyt
True 0.6863 0.625700 13
10
ginal
True 0.6658 0.601900 19
11
Teapot
True 0.6426 0.567400 7
12
Kamilo
True 0.6074 0.528100 15

The provided data consist of eleven microarray sets with a various number of instances and attributes. Microarray data is a typical example of a problem called "few-samples-many-attributes". 

The data tables are provided as CSV files with the ',' (coma) separator sign. In each set, the last column is called "target" and contains class labels for samples. The data sets can be downloaded after registration to the competition. You only have access to the training parts of the data sets. Your task is to (for each set) identify the optimal subset of attributes for a KNN classifier with a biweight kernel and the k parameter set to 11. No additional regularization will be used for the model. For each data set, you may indicate between 2 and 102 attributes but a small penalty to your score will be added for each attribute used.

The evaluation metric will be balanced accuracy (BAC) adjusted by a penalty for using many attributes. In particular, your score is the average of BAC - (d-2)/1000, where d is the number of utilized attributes for a given data set.

SVM model used for the evaluation is computed with the code below:

model <- kknn(target~., 
              data.table(dt_tr[, feats, with = FALSE], target = dt_tr[, factor(target)]),
              dt_te,
              k = 11, distance = 2, kernel = "biweight")

During the competition, your solutions will be evaluated on small random subsets of the test data parts, and your best preliminary score will be displayed on the public Leaderboard. The final score of each team will be computed on the remaining parts of the test data sets.

The submission format: the solutions need to be submitted as text files with indicated attribute sets. The file should have exactly 11 rows. In each row, it should contain integers between 1 and the number of columns in the corresponding data set, separated by commas. These integers should indicate attributes (column numbers) that will be used by the evaluation model. The ordering of rows should correspond to the numbers indicated in the names of the provided data sets.

The deadline for sending submissions and reports is January 27, 2024.

In order to download competition files you need to be enrolled.
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