Source codes of algorithms and datasets for our paper "Improved Regret Bounds for Online Kernel Selection under Bandit Feedback", accepted in ECML-PKDD 2022.
We implement all algorithms with R on a Windows machine with 2.8 GHz Core(TM) i7-1165G7 CPU. execute each experiment 10 times with random permutation of all datasets and average all of the results.
The default path of codes is "D:/experiment/Conference Paper/ECML/ECML2022". The path of datasets is "D:/experiment/online learning dataset/regression/" or "D:/experiment/online learning dataset/binary C/". The store path is "D:/experiment/Conference Paper/ECML/ECML2022/ECML2022 Result/". You can also change all of the default paths.
The baseline algorithms include: OKS, RF-OKS, Raker and LKMBooks. Our algorithms include: OKS++, IOKS, RF-OKS++ and RF-IOKS.
The datasets are downloaded from: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ and http://archive.ics.uci.edu/ml/datasets.php
binary classification datasets: magic04 (Num:19020, Fea: 10), phishing (Num: 11055, Fea: 68), a9a (Num: 32561, Fea: 123), SUSY (Num: 20000, Fea: 18).
regression datasets bank (Num:8192, Fea: 32), elevators (Num:16599, Fea: 18), ailerons (Num:13750, Fea: 40), Hardware (Num:28179, Fea: 96)