NiaClass: Building Rule-Based Classification Models Using Nature-Inspired Algorithms

[1]  Vili Podgorelec,et al.  Improved Nature-Inspired Algorithms for Numeric Association Rule Mining , 2021, ICO.

[2]  Iztok Fister,et al.  On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification , 2020, ICCS.

[3]  N. Srinivasu,et al.  Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification , 2020, Data Knowl. Eng..

[4]  Alejandro Barredo Arrieta,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2019, Inf. Fusion.

[5]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[6]  Christian Blum,et al.  Swarm Intelligence: Introduction and Applications , 2008, Swarm Intelligence.

[7]  Monique Snoeck,et al.  Classification With Ant Colony Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[8]  Tiago Ferra de Sousa,et al.  Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..

[9]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2015, Natural Computing Series.

[12]  Bart Baesens,et al.  Editorial survey: swarm intelligence for data mining , 2010, Machine Learning.