Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.

We propose a pathogen-classification system using the Surface-Enhanced Raman Scattering (SERS) platform. The system differentiates the pathogens based on their SERS spectra, which are believed to be related to the surface chemical components. The specialty of the system is to not only consider the usual classification accuracy, but also pay attention to the different types of costs during misclassification. For instance, due to the effectiveness of treatments, the cost of classifying a Gram-positive bacterium as another Gram-positive one should be lower than the cost of classifying a Gram-positive bacterium as a Gram-negative one. We express the task as the cost-sensitive classification problem, and take state-of-the-art cost-sensitive classification algorithms from the machine learning community to conquer the task. Our experimental study validates the usefulness of those algorithms on building the system.