Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP.