TY - GEN
T1 - Novelty-Organizing Classifiers applied to classification and reinforcement learning
T2 - 16th Genetic and Evolutionary Computation Conference, GECCO 2014
AU - Vargas, Danilo Vasconcellos
AU - Takano, Hirotaka
AU - Murata, Junichi
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - It is widely known that reinforcement learning is a more general problem than supervised learning. In fact, supervised learning can be seen as a class of reinforcement learning problems. However, only a couple of papers tested reinforcement learning algorithms in supervised learning problems. Here we propose a new and simpler way to abstract supervised learning for any reinforcement learning algorithm. Moreover, a new algorithm called Novelty-Organizing Classifiers is developed based on a Novelty Map population that focuses more on the novelty of the inputs than their frequency. A comparison of the proposed method with Self-Organizing Classifiers and BioHel on some datasets is presented. Even though BioHel is specialized in solving supervised learning problems, the results showed only a trade-off between the algorithms. Lastly, results on a maze problem validate the flexibility of the proposed algorithm beyond supervised learning problems. Thus, Novelty-Organizing Classifiers is capable of solving many supervised learning problems as well as a maze problem without changing any parameter at all. Considering the fact that no adaptation of parameters was executed, the proposed algorithm's basis seems interestingly flexible.
AB - It is widely known that reinforcement learning is a more general problem than supervised learning. In fact, supervised learning can be seen as a class of reinforcement learning problems. However, only a couple of papers tested reinforcement learning algorithms in supervised learning problems. Here we propose a new and simpler way to abstract supervised learning for any reinforcement learning algorithm. Moreover, a new algorithm called Novelty-Organizing Classifiers is developed based on a Novelty Map population that focuses more on the novelty of the inputs than their frequency. A comparison of the proposed method with Self-Organizing Classifiers and BioHel on some datasets is presented. Even though BioHel is specialized in solving supervised learning problems, the results showed only a trade-off between the algorithms. Lastly, results on a maze problem validate the flexibility of the proposed algorithm beyond supervised learning problems. Thus, Novelty-Organizing Classifiers is capable of solving many supervised learning problems as well as a maze problem without changing any parameter at all. Considering the fact that no adaptation of parameters was executed, the proposed algorithm's basis seems interestingly flexible.
UR - http://www.scopus.com/inward/record.url?scp=84905667096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905667096&partnerID=8YFLogxK
U2 - 10.1145/2598394.2598429
DO - 10.1145/2598394.2598429
M3 - Conference contribution
AN - SCOPUS:84905667096
SN - 9781450328814
T3 - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
SP - 81
EP - 82
BT - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery
Y2 - 12 July 2014 through 16 July 2014
ER -