Soft computing techniques and its applications extend its wings in almost all areas which include data mining, pattern discovery, industrial applications, robotics, automation and many more. Soft computing comprises of the core components such as fuzzy logic, genetic algorithm, artificial neural networks, and probabilistic reasoning. In spite of these, recently many bio-inspired computing attracted attention for the researchers to work in that area. Machine learning plays an important role in the design and development of decision support systems, applied soft computing and expert systems applications. This research work aims to build an artificial Lampyridae classifier and also compared with Takagi Sugeno Kang fuzzy classifier and ANN classifier in terms of prediction accuracy, sensitivity, specificity, and Mathew’s correlation coefficient. The significance of MCC is to test the ability of the machine learning classifier in spite of other performance metrics. Implementations are done in Scilab and from the obtained results it is inferred that the built ALC outperforms that that of TSK fuzzy classifier and ANN classifier.