The diversified strategy can reduce the systematic risk efficiently, but may fail to account for emergent and default risk that many decision-makers usually face at large-scale level. Modern data-driven methodologies allow optimizing both systematic and non-systematic risks in a unified framework. In this article, we demonstrate an approach to analyze and compare partial-diversified portfolios of Credit Default Swap. We classify and investigate different metrics of credit risks and integrate them with limited diversification and other performance objectives. We test the developed approach in a study of hundreds of business contract investments over the recent financial crisis. The results indicate that the decisions using limited diversification are more robust in terms of allocation structure and out-of-sample downside risks reduction. Therefore, the partial-diversified optimization models provide alternatives to support a variety of problems involving unknown risks.