Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 923-929, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
10 Jun 2016
K. Suzuki1, M. Claesen2, H. Takeda1, and B. De Moor2 1Dept. of R&D, KOKUSAI KOGYO CO.,LTD., 2-24-1 Harumi-cho, Fuchu-shi,Tokyo,183-0057, Japan
2Dept. of Electrical Engineering, KU Leuven, STADIUS Kasteelpark Arenberg 10, 3001 Leuven, Belgium
Keywords: Automated Machine Learning, Feature Generation, Classification, Meta-heuristic Hyperparameter Optimization, Particle Swarm Optimization, UC Merced Land Use data set Abstract. Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.
Conference paper (PDF, 1069 KB)

Citation: Suzuki, K., Claesen, M., Takeda, H., and De Moor, B.: A FULLY AUTOMATED PIPELINE FOR CLASSIFICATION TASKS WITH AN APPLICATION TO REMOTE SENSING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 923-929,, 2016.

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