A supervised machine-learning approach towards geochemical predictive modelling in archaeology

Oonk, Stijn; Spijker, J.

doi:10.1016/j.jas.2015.04.002

Cited by 47 publications

(26 citation statements)

References 31 publications

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“…In contrast, the tested supervised methods, through a machine learning approach, have been able to develop predictive models of provenance with accuracies above 80%, sometimes has high as 88.1%. Such level of accuracy can be considered very high, actually accuracies around 75% have been considered enough to rate a predictive model on soil prediction as successful [26]. This potentially opens the possibility for developing a tool that could predict the class of unknown samples, with an about 90% of accuracy, that could be used to certify the provenance of pottery productions with that level of probability.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast the use of supervised methods is quite scarce. However, some incursions within the supervised methods domain had also been done previously, from the pioneering works of [17,25] to much more recent papers [26][27][28] that focus particularly on the artificial neural network (ANN) approach.…”

Section: Data Processingmentioning

confidence: 99%

“…Taking into account that the provenance of the analyzed samples is known (i.e., the class labels are known for each object) supervised models can also be used to process the data. Starting from the whole experimental dataset, it is possible to constitute a training dataset [26]. The labelled geochemical data is then used to build models that proxy for class characteristics.…”

Section: Data Processingmentioning

confidence: 99%

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Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

Anglisano

Casas

Anglisano³

et al. 2019

Minerals

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The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO 2 and CaCO 3 concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples.

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Section: Discussionmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

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Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

Anglisano

Casas

Anglisano³

et al. 2019

Minerals

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“…They can be used for recognition and identifying the things in places where known methods are not capable of solving problems. During the years lots of neural networks [2][3][4] were developed and each one of them can be used to solve specific tasks. Each artificial neural network is composed of simple processing elements called neurons ( Figure 1) [1,5] connected with each other to arrange in a layer.…”

Section: Introductionmentioning

confidence: 99%

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

Petkov

Mustafa

Sotirov

et al. 2016

Acta Scientifica Naturalis

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A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography -mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, "unknown" were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety "unknown" samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

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“…In machine learning community, the problems of pattern classification and regression has been studied well. Classification and regression are two most popular supervised learning problems [33,18,4,24,39,17,25,31,37,36,28,29,30,27,26]. In these problems, we usually have a training set of input-output pairs.…”

Section: Introductionmentioning

confidence: 99%

Manifold regularization in structured output space for semi-supervised structured output prediction

Jiang

Jia

Sheng

et al. 2015

Neural Comput & Applic

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Structured output prediction aims to learn a predictor to predict a structured output from a input data vector. The structured outputs include vector, tree, sequence, etc. We usually assume that we have a training set of input-output pairs to train the predictor. However, in many real-world applications, it is difficult to obtain the output for a input, thus for many training input data points, the structured outputs are missing. In this paper, we discuss how to learn from a training set composed of some input-output pairs, and some input data points without outputs. This problem is called semisupervised structured output prediction. We propose a novel method for this problem by constructing a nearest neighbor graph from the input space to present the manifold structure, and using it to regularize the structured output space directly. We define a slack structured output for each training data point, and proposed to predict it by learning a structured output predictor. The learning of both slack structured outputs and the predictor are unified within one single minimization problem. In this problem, we propose to minimize the structured loss between the slack structured outputs of neighboring data points, and the prediction error measured by the structured loss. The problem is optimized by an iterative algorithm. Experiment results over three benchmark data sets show its advantage.

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A supervised machine-learning approach towards geochemical predictive modelling in archaeology

Cited by 47 publications

References 31 publications

Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

Manifold regularization in structured output space for semi-supervised structured output prediction

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