MALDI-TOF mass spectrometry has high social and economic value in rapid identification of microorganisms based on the protein mass profile represented in a mass spectrum of the microorganism. Numerous studies have been conducted to identify microorganisms using MALDI-TOF MS. Markers are characteristics that can be used to uniquely distinguish microorganisms. Microorganisms can be identified by applying markers selected based on the extracted mass information. Previous studies demonstrated that combining mass information extracted by MALDI-TOF MS with machine-learning techniques can improve microorganism classification. Classification of microorganisms is particularly difficult and critical for mycobacteria because various pathogens should be treated with different prescriptions, although they exhibit similar compositions. It is quite challenging to accurately identify mycobacteria using conventional methods because their MALDI-TOF MS patterns are similar to each other. In this study, we propose a support vector machine model for improving the distinction of similar species by learning positive and negative markers separately extracted in each group. We classified species in the Mycobacterium abscessus group and Mycobacterium fortuitum group. Our novel approach applies negative markers to classify similar species and improves the identification of similar species using a combination of positive and negative markers.