CoABCMiner: An Algorithm for Cooperative Rule Classification System Based on Artificial Bee Colony

Çelik M., Köylü F., Karaboğa D.

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, cilt.25, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1142/s0218213015500281
  • Dergi Adı: INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Data mining, machine learning, cooperative rule learning, token competition, artificial bee colony, supervised learning, PARTICLE SWARM OPTIMIZATION, COEVOLUTIONARY ALGORITHM, TURNING OPERATIONS, GENETIC ALGORITHMS, DECISION RULES, DESIGN, INDUCTION, MODELS, COMPETITION, PARAMETERS
  • Erciyes Üniversitesi Adresli: Evet

Özet

In data mining, classification rule learning extracts the knowledge in the representation of IF THEN rule which is comprehensive and readable. It is a challenging problem due to the complexity of data sets. Various meta-heuristic machine learning algorithms are proposed for rule learning. Cooperative rule learning is the discovery process of all classification rules with a single run concurrently. In this paper, a novel cooperative rule learning algorithm, called CoABCMiner, based on Artificial Bee Colony is introduced. The proposed algorithm handles the training data set and discovers the classification model containing the rule list. Token competition, new updating strategy used in onlooker and employed phases, and new scout bee mechanism are proposed in CoABCMiner to achieve cooperative learning of different rules belonging to different classes. We compared the results of CoABCMiner with several state-of-the-art algorithms using 14 benchmark data sets. Non parametric statistical tests, such as Friedman test, post hoc test, and contrast estimation based on medians are performed. Nonparametric tests determine the similarity of control algorithm among other algorithms on multiple problems. Sensitivity analysis of CoABCMiner is conducted. It is concluded that CoABCMiner can be used to discover classification rules for the data sets used in experiments, efficiently.