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Designing adequate and precise neural architectures is a challenging task,often done by highly specialized personnel. AutoML is a machine learning fieldthat aims to generate good performing models in an automated way. Spectral datasuch as those obtained from biological analysis have generally a lot ofimportant information, and these data are specifically well suited toConvolutional Neural Networks (CNN) due to their image-like shape. In this workwe present NASirt, an AutoML methodology based on Neural Architecture Search(NAS) that finds high accuracy CNN architectures for spectral datasets. Theproposed methodology relies on the Item Response Theory (IRT) for obtainingcharacteristics from an instance level, such as discrimination and difficulty,and it is able to define a rank of top performing submodels. Severalexperiments are performed in order to demonstrate the methodology's performancewith different spectral datasets. Accuracy results are compared to otherbenchmarks methods, such as a high performing, manually crafted CNN and theAuto-Keras AutoML tool. The results show that our method performs, in mostcases, better than the benchmarks, achieving average accuracy as high as97.40%.

NASirt: AutoML based learning with instance-level complexity information