An Empirical Study on Classification Using Modified Teaching Empirical Study on Classification Using Modified Teaching Empirical Study on Classification Using Modified Teaching Empirical Study on Classification Using Modified Teaching Learning Based Optimization Learning Based Optimization Learning Based Optimization Learning Based Optimization
Amaresh Sahu, S.K. Panigrahi, Sabyasachi Pattnaik · 2013
In this paper the modification to ‘Teaching–Learning Based Optimization (TLBO) called Modified Teaching–Learning Based Optimization (MTLBO) based on particle swarm optimization principle has been proposed. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution. The process of MTLBO is divided into two parts, the first part consists of the ‘Teacher Phase’ means learning from the teacher and the second part consists of the ‘Learner Phase’ means learner learns by interacting with other learner having better knowledge and from the best learner knowledge treated as team leader among all learners. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on classification using neural network in data mining.