From 66a778b9068ad14dd165551b52c582e5fabafe9a Mon Sep 17 00:00:00 2001
From: kliegr <6094393+kliegr@users.noreply.github.com>
Date: Fri, 13 Sep 2019 16:09:45 +0200
Subject: [PATCH] Updated paper info

---
 README.md | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index d03693a..70cf3e6 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,11 @@ pyIDS is a custom implementation of IDS (Interpretable Decision Sets) algorithm
 
 ```LAKKARAJU, Himabindu; BACH, Stephen H.; LESKOVEC, Jure. Interpretable decision sets: A joint framework for description and prediction. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2016. p. 1675-1684.```
 
-If you find this package useful in your research, please cite [our article](https://nb.vse.cz/~klit01/papers/RuleML_Challenge_IDS.pdf).
+If you find this package useful in your research, please cite our paper on this [Interpretable Decision Sets Implementation](https://nb.vse.cz/~klit01/papers/RuleML_Challenge_IDS.pdf):
+
+    Jiri Filip, Tomas Kliegr. PyIDS - Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016. RuleML+RR2019@Rule Challenge 2019. http://ceur-ws.org/Vol-2438/paper8.pdf
+
+
 
 # Examples
 
@@ -118,4 +122,4 @@ dataframes = [ pd.read_csv("./data/iris{}.csv".format(i)) for i in range(10)]
 
 kfold = KFoldCV(IDS(), dataframes)
 scores = kfold.fit(rule_cutoff=50)
-```
\ No newline at end of file
+```