This study embedded a recursive feature selection scheme in support vector machines (SVM) for credit rating forecasting. SVMs have been successfully applied in numerous areas, and have demonstrated excellent performance. However, due to the high dimensionality of our input variables, this study employed a fast recursive feature selection algorithm to eliminate irrelevant features and enhance the performance of SVM classifiers. Empirical results have indicated that one-vs-one SVM with embedded recursive feature selection outperforms other multi-class SVMs. Compared to traditional multi-class classifiers, the performance improvement owing to embedded recursive feature selections is significant.

Abstract-Inthis article, a contribution-enabled congestion-aware (CECA) scheme is proposed to enhance the fairness and efficiency of content distribution, and improve the entire bandwidth utility for mesh peer-to-peer (P2P) real-time multimedia streaming systems. This is because the mesh P2P streaming system outperforms others in many aspects, but most content distribution mechanisms use only one of upload bandwidth and link latency as a factor that malfunctions the reward and punishment scheme. Besides, their congestion controls partially or mostly rely on the transport layer protocol such as transmission control protocol (TCP). Hence, the CECA dynamically adjusts a receiver peer's content window size (CWS) based on the contribution and packet loss rate of the receiver peer. Furthermore, the CECA is implemented in NS2 and experimental results show that the CECA could at the most shorten the receiving time about 20.78%, 32.26%, 30.74%, and 26.18%, and improve the entire performance about 11.42%, 18.87%, 14.55%, and 14.19% at the most, when it compares with the BLACE, the Coolstreaming, the Coolstreaming with bandwidth preference, and the Coolstreaming with latency preference, respectively.

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