Search results for "Statistica"
showing 10 items of 5969 documents
Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix
2020
In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. The last years have seen a remarkable flowering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be proposed in order to extract useful info…
Entropy-Based Classifier Enhancement to Handle Imbalanced Class Problem
2017
The paper presents a possible enhancement of entropy-based classifiers to handle problems, caused by the class imbalance in the original dataset. The proposed method was tested on synthetic data in order to analyse its robustness in the controlled environment with different class proportions. As also the proposed method was tested on the real medical data with imbalanced classes and compared to the original classification algorithm results. The medical field was chosen for testing due to frequent situations with uneven class ratios.
Are nonlinear model-free conditional entropy approaches for the assessment of cardiac control complexity superior to the linear model-based one?
2016
Objective : We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals. Methods : An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k -nearest-neighbor CE procedures and permutation CE. Electroca…
Characterization of entropy measures against data loss: Application to EEG records
2012
This study is aimed at characterizing three signal entropy measures, Approximate Entropy (ApEn), Sample Entropy (SampEn) and Multiscale Entropy (MSE) over real EEG signals when a number of samples are randomly lost due to, for example, wireless data transmission. The experimental EEG database comprises two main signal groups: control EEGs and epileptic EEGs. Results show that both SampEn and ApEn enable a clear distinction between control and epileptic signals, but SampEn shows a more robust performance over a wide range of sample loss ratios. MSE exhibits a poor behavior for ratios over a 40% of sample loss. The EEG non-stationary and random trends are kept even when a great number of samp…
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …
Non-equilibrium Markov state modeling of periodically driven biomolecules
2019
Molecular dynamics simulations allow to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external p…
Measuring the weather’s impact on MAC layer over 2.4GHz outdoor radio links
2015
The weather s impact on the performance of a radio link at the 2.4 GHz ISM (Industry, Scientific and Medical) band had not been yet studied in great detail up to now as it is generally believed that the ultra-high frequency band is not significantly affected by weather conditions. However, our study shows significant correlations between meteorological variables and control frame errors at MAC (Medium Access Control) layer of the IEEE 802.11b/g standard. This study is performed over an outdoor radio link setting which has been monitored for several months. Moreover, we check if link distance and so modulation scheme and data rate are also decisive features of such impact. Our real scenario …
ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability.
2020
In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. Th…
Molecular dynamics simulations in hybrid particle-continuum schemes: Pitfalls and caveats
2017
Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations typically pose a computational bottleneck, which we investigate in detail in this study. We find that it is preferable to simulate many small systems as opposed to a few large systems, and that a choice of a simple isokinetic thermostat is typically sufficient while thermostats such as Lowe-Andersen allow for simulations at elevated viscosity. We discuss suitable choices for time steps and finite-size effects which arise in the limit of very small simulation bo…
Spreading of Competing Information in a Network
2020
We propose a simple approach to investigate the spreading of news in a network. In more detail, we consider two different versions of a single type of information, one of which is close to the essence of the information (and we call it good news), and another of which is somehow modified from some biased agent of the system (fake news, in our language). Good and fake news move around some agents, getting the original information and returning their own version of it to other agents of the network. Our main interest is to deduce the dynamics for such spreading, and to analyze if and under which conditions good news wins against fake news. The methodology is based on the use of ladder fermion…