Addressing the National Academy of Sciences’ Challenge: A Method for Statistical Pattern Comparison of Striated Tool Marks
Addressing the National Academy of Sciences’ Challenge: A Method for Statistical Pattern Comparison of Striated Tool Marks
复制标题
应对美国国家科学院的挑战：条纹工具标记统计模式比较的方法
DOI：
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发表时间：
2012
影响因子：
1.6
通讯作者：
N. Petraco
中科院分区：
文献类型：
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作者：
N. Petraco;P. Shenkin;Jacqueline Speir;Peter Diaczuk;P. A. Pizzola;Carol Gambino;N. Petraco
摘要：2009年2月，美国国家科学院发表了一份题为《加强美国法医学：前进之路》的报告。报告指出，必须进行研究，以“了解法医科学中常用的比较方法的可靠性和可重复性”。数值分类方法能够为这些词分配客观的定量度量。在这项研究中，用9个开槽螺丝刀制作了可重复的理想条纹图案，编码成高维特征向量，并进行了多种统计模式识别方法。选择所采用的具体方法是因为它们长期的同行评审记录，在工业和学术应用中广泛成功的使用，依赖于对数据潜在分布的很少假设，可以伴随着标准置信度，并且是可证伪的。对于PLS - DA，通过仅保留数据的8个维度（8D），可以实现97%或更高的正确分类率。对于相同的性能水平，PCA - SVM需要更少的维数（4D）。最后，首次在法医学，它显示了如何使用保形预测理论来计算识别条纹模式在给定的置信水平。
Abstract: In February 2009, the National Academy of Sciences published a report entitled “Strengthening Forensic Science in the United States: A Path Forward.” The report notes research studies must be performed to “…understand the reliability and repeatability…” of comparison methods commonly used in forensic science. Numerical classification methods have the ability to assign objective quantitative measures to these words. In this study, reproducible sets of ideal striation patterns were made with nine slotted screwdrivers, encoded into high‐dimensional feature vectors, and subjected to multiple statistical pattern recognition methods. The specific methods employed were chosen because of their long peer‐reviewed track records, widespread successful use for both industry and academic applications, rely on few assumptions on the data’s underlying distribution, can be accompanied by standard confidence levels, and are falsifiable. For PLS‐DA, correct classification rates of 97% or higher were achieved by retaining only eight dimensions (8D) of data. PCA‐SVM required even fewer dimensions, 4D, for the same level of performance. Finally, for the first time in forensic science, it is shown how to use conformal prediction theory to compute identifications of striation patterns at a given level of confidence.