The evaluation of classifiers' performances plays a critical role in construction and selection of classification model. Although many performance metrics have been proposed in machine learning community, no general guidelines are available among practitioners regarding which metric to be selected for evaluating a classifier's performance. In this paper, we attempt to provide practitioners with a strategy on selecting performance metrics for classifier evaluation. Firstly, the authors investigate seven widely used performance metrics, namely classification accuracy, F-measure, kappa statistic, root mean square error, mean absolute error, the area under the receiver operating curve, and the area under the precision-recall curve. Secondly, the authors resort to using Pearson linear correlation and Spearman rank correlation to analyses the potential relationship among these seven metrics. Experimental results show that these commonly used metrics can be divided into three groups, and all metrics within a given group are highly correlated but less correlated with metrics from different groups.
In this paper, we propose a watermarking algorithm for digital image based on DCT and SVD. The algorithm can satisfy the transparence and robustness of the watermarking system very well. The experiment based on this algorithm demonstrates that the watermarking is robust to the common signal processing techniques including JEPG compressing, noise, low pass filter, median filter, contrast enhance .Experimental results show that the new watermarking scheme is more robust than the SVD methods.2008 Congress on Image and Signal Processing 978-0-7695-3119-9/08 $25.00
The problem of learning label rankings is receiving increasing attention from machine learning and data mining community. Its goal is to learn a mapping from instances to rankings over a finite number of labels. In this paper, we devote to giving an overview of the state-of-the-art in the area of label ranking, and providing a basic taxonomy of the label ranking algorithms. Specifically, we classify these label ranking algorithms into four categories, namely decomposition methods, probabilistic methods, similaritybased methods, and other methods. We pay particular attention to the latest advances in each. Also, we discuss their strengths and weaknesses, and highlight some interesting challenges that remain to be solved.
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