Selected article for: "classification performance and low number"
Author: Saban Ozturk; Umut Ozkaya; Mucahid Barstugan
Title: Classification of Coronavirus Images using Shrunken Features
  • Document date: 2020_4_6
    • ID: 2l1zw19o_58
    Snippet: For this reason, classifier performance can be misleading. However, when all comparisons are examined, the common contribution cannot be ignored. The results of the sAE method, which produces favorable results for many studies in the literature, are surprising. The main reasons for this are the low number of samples, the imbalance between classes, and the closeness in synthetic data. When this situation causes in-class affinity, it creates a code.....
    Document: For this reason, classifier performance can be misleading. However, when all comparisons are examined, the common contribution cannot be ignored. The results of the sAE method, which produces favorable results for many studies in the literature, are surprising. The main reasons for this are the low number of samples, the imbalance between classes, and the closeness in synthetic data. When this situation causes in-class affinity, it creates a code generation problem for sAE. It appears that it is not appropriate to use a CNN architecture for the training of such datasets, which is insufficient even for a shallow sAE architecture. Similar to the approach to hand-crafted feature extraction techniques, PCA architecture was considered instead of the sAE architecture. Since the PCA architecture can operate independently from the number of samples, it has provided very successful classification performance.

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