I have been classifying series of data that are cyclic, as in not exactly periodic but repeating. As a form of data augmentation, I've been trying to separately classify the single cycles rather than the whole series. To get the final class score, I average the scores (before softmax) of the cycles belonging to the same series. This approach seem to yield very good results, for some reasons I believe:
Smaller input data leads to a smaller model, and segmenting the input increases the available data
Focusing on a single period seems to make the classifier highlight better features from saliency maps
Combining the output of the classifier can be beneficial, as in if one cycle is corrupted and wrongly classified the others may compensate from it. This probably happens even when classifying the whole time series, but with the segmentation is more explicit.
Has this been done in any other work? Am I falling into some kind of fallacy by applying this segmentation?
SnowTime11 t1_j2r5jk0 wrote
Reply to [D] Simple Questions Thread by AutoModerator
I have been classifying series of data that are cyclic, as in not exactly periodic but repeating. As a form of data augmentation, I've been trying to separately classify the single cycles rather than the whole series. To get the final class score, I average the scores (before softmax) of the cycles belonging to the same series. This approach seem to yield very good results, for some reasons I believe:
Smaller input data leads to a smaller model, and segmenting the input increases the available data
Focusing on a single period seems to make the classifier highlight better features from saliency maps
Combining the output of the classifier can be beneficial, as in if one cycle is corrupted and wrongly classified the others may compensate from it. This probably happens even when classifying the whole time series, but with the segmentation is more explicit.
Has this been done in any other work? Am I falling into some kind of fallacy by applying this segmentation?