审稿人提出一个问题:
The most serious issue: The results evidence in the manuscript is made by Accuracy metric. But if you measure TP, TN, FP, FN, then you suppose to classify the only label at the test. You have 6 labels totally (Walking forward, Standing, Sitting, Walking in place, Running in place, Jumping) and have tested them separately. Hence you usually had about 1/6 positive outputs among total. If so, the Accuracy metric does not show the efficiency of your classifier due to narrow hypothesis effect. For instance, you are testing Sitting and you have 1000 samples with 150 labeled as Sitting. Then if your classifier gives
TP = 10, TN = 850, FP = 0 and FN = 140,
you'll receive Acc = 86%, whereas you have really detected just 1/15 of target cases. You should describe you experiment in details and make your conclusions based on the relevant metrics.
机翻了一下:稿件中的结果证据是通过准确性指标做出的。但是,如果您测量TP,TN,FP,FN,那么假设在测试中对唯一的标签进行分类。您总共有 6 个标签(向前走、站立、坐下、原地行走、原地跑步、跳跃),并分别对其进行了测试。因此,您通常总共有大约 1/6 的正输出。如果是这样,由于狭义假设效应,准确度指标不会显示分类器的效率。例如,您正在测试坐姿,您有 1000 个样本,其中 150 个标记为坐姿。然后,如果您的分类器给出
TP = 10, TN = 850, FP = 0 和 FN = 140,
您将收到 Acc = 86%,而您实际上只检测到 1/15 的目标病例。您应该详细描述您的实验,并根据相关指标得出结论。
这是什么意思。
”If so, the Accuracy metric does not show the efficiency of your classifier due to narrow hypothesis effect“