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H. Kholerdi, N. TaheriNejad, A. Jantsch:
"Enhancement of Classification of Small Data Sets Using Self-Awareness - an Iris Flower Case-Study";
in: "2018 IEEE International Symposium on Circuits and Systems (ISCAS)", issued by: IEEE; IEEE International Symposium on Circuits and Systems (ISCAS), Florence, 2018, 1 - 5.

In big-data (Deep) Neural Network (NN) algorithm is often used for classification. However, such a massive mine of data is not always available and a shortage of training data can significantly deteriorate the performance of NNs and other classifiers. Therefore, we propose a self-aware multiple classifier system suitable for "Small-Data" cases. This algorithm uses self-awareness to switch between classifiers to improve its performance. We tested the algorithm for the classification of iris flower species using the Iris standard database. Compared to NN, our algorithm showed up to 17% classification success rate improvement with up to 10 times smaller standard deviation.

Machine learning, classification, self-awareness, confidence, neural networks, naive Bayesian, support vector machine

http://dx.doi.org/10.1109/ISCAS.2018.8350992