9,848 research outputs found
Classification of Hungarian medieval silver coins using x-ray fluorescent spectroscopy and multivariate data analysis
A set of silver coins from the collection of Déri Museum Debrecen (Hungary) was examined by X-ray
fluorescent elemental analysis with the aim to assign the coins to different groups with the best possible precision
based on the acquired chemical information and to build models, which arrange the coins according to their
historical periods.
Results: Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis,
classification and regression trees and multivariate curve resolution with alternating least squares were applied to
reveal dominant pattern in the data and classify the coins into several groups. We also identified those chemical
components, which are present in small percentages, but are useful for the classification of the coins. With the
coins divided into two groups according to adequate historical periods, we have obtained a correct classification
(76-78%) based on the chemical compositions.
Conclusions: X-ray fluorescent elemental analysis together with multivariate data analysis methods is suitable to
group medieval coins according to historical periods.
Keywords: X-ray fluorescence spectroscopy, Multivariate techniques, Coin, Silver, Middle age
Multi-test Decision Tree and its Application to Microarray Data Classification
Objective:
The desirable property of tools used to investigate biological data is
easy to understand models and predictive decisions.
Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity.
Methods:
We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions.
Results:
Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on datasets by an average percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model
are supported by biological evidence in the literature.
Conclusion:
This paper introduces a new type of decision tree which is more suitable for solving biological problems.
MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts
Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
In recent years, Imaging Atmospheric Cherenkov Telescopes (IACTs) have
discovered a rich diversity of very high energy (VHE, > 100 GeV) gamma-ray
emitters in the sky. These instruments image Cherenkov light emitted by
gamma-ray induced particle cascades in the atmosphere. Background from the much
more numerous cosmic-ray cascades is efficiently reduced by considering the
shape of the shower images, and the capability to reduce this background is one
of the key aspects that determine the sensitivity of a IACT. In this work we
apply a tree classification method to data from the High Energy Stereoscopic
System (H.E.S.S.). We show the stability of the method and its capabilities to
yield an improved background reduction compared to the H.E.S.S. Standard
Analysis.Comment: 10 pages, 9 figures, accepted for publication in Astroparticle
Physic
- …