Recognizing Human Emotions by using Lazy classifier with selected algorithm

Music predates language, and humans are born with the ability to communicate themselves via music. Music has played a vital part in human history, and it is incorporated into many facets of human life Music is showing tremendously significant role in people’s production and life. This research is focusing a n identification of human emotions like happiness, sad, relaxation, tension on Turkish music emotion dataset through inductive learning approach. This work is to focus on the various lazy learning approaches are implemented to recognize human emotions while listening music. The K=3 with Ball Tree NN search model acquires highest performance which as accuracy 85.75%, kappa 0.66, precision 0.86, F-Measure0.86, recall 0.86, Matthews Correlation Coefficient 0.65 which generated the best outcomes based on their efficacy in comparison to other models. The K=3 with Ball Tree model produces best result for recognizing human emotions compare with other models