Machine Learning and data mining tools applied for databases of low number of records

Abstract

The use of data mining and machine learning tools is becoming increasingly common. Their usefulness is mainly noticeable in the case of large datasets, when information to be found or new relationships are extracted from information noise. The development of these tools means that datasets with much fewer records are being explored, usually associated with specific phenomena. This specificity most often causes the impossibility of increasing the number of cases, and that can facilitate the search for dependences in the phenomena under study. The paper discusses the features of applying the selected tools to a small set of data. Attempts have been made to present methods of data preparation, methods for calculating the performance of tools, taking into account the specifics of databases with a small number of records. The techniques selected by the author are proposed, which helped to break the deadlock in calculations, i.e., to get results much worse than expected. The need to apply methods to improve the accuracy of forecasts and the accuracy of classification was caused by a small amount of analysed data. This paper is not a review of popular methods of machine learning and data mining; nevertheless, the collected and presented material will help the reader to shorten the path to obtaining satisfactory results when using the described computational methods

Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks

“…The model we developed is generally not inferior to the above models in terms of the analyzed indicators [53], which suggests that crack inspection software for construction and facility managers using stateof-the-art segmentation will demonstrate an IoU level greater than 0.80 when detecting building façade cracks. The level of IoU = 0.84 for the best model in our study is not inferior to the technology under consideration and is also higher than this indicator in the model of pavement crack segmentation based on deep learning in [54,55], where IoU of 0.6235 for the Crack500 data set and 0.5278 for MCD data set is noted. 2.…”