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Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly\udpushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the\udbackground in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view,\udutilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any\udspecial knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a\udwhole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with\udoptimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms\udfor the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine\udappropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained\uduntouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets,\udcommon datasets in computer vision field for character recognition and object recognition problems respectively, our automated\udlearning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small\udamount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without\udalmost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high\udpossibility that our approach contributes to general classification problems

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A FULLY AUTOMATED PIPELINE FOR CLASSIFICATION TASKS WITH AN APPLICATION TO REMOTE SENSING