Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance in terms of predictive accuracy. This explains why, over the past decade, there have been many exten...

This paper presents a new approach to regional myocardial contractility analysis based on inter-landmark motion (ILM) vectors and multivariate outlier detection. The proposed spatio-temporal representation is used to describe the coupled changes occurring at pairs of regions of the left ventricle, thus enabling the detection of geometrical and dynamic inconsistencies. Multivariate tolerance reg...

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics - that is the combination of landscape ecology with population genomics - include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null mod...

Identification of outliers in multivariate data is not trivial. especially when there exists several outliers in the data. The classical identification method based on the sample mean and sample covariance matrix cannot always find them, because the classicd rnean and covariance matris are themselves affected by outliers. This problem is termed as masting7 because the outliers get maslied by ea...

MOTIVATION As the use of microarrays in human studies continues to increase, stringent quality assurance is necessary to ensure accurate experimental interpretation. We present a formal approach for microarray quality assessment that is based on dimension reduction of established measures of signal and noise components of expression followed by parametric multivariate outlier testing. RESULTS...

Principal component analysis (PCA) is a widely adopted multivariate data analysis technique, with interpretation being established on the basis of both classical linear projection and a probability model (i.e. probabilistic PCA (PPCA)). Recently robust PPCA models, by using the multivariate t distribution, have been proposed to consider the situation where there may be outliers within the data ...

In this paper we introduce the unsupervised machine-learning algorithm named Local Outlier Factor (LOF), for health risk assessment. In general the LOF algorithm is used with numerical attributes and the outcome of the algorithm is parting the patterns into normal and abnormal events. In this paper we introduce the extended LOF algorithm with three experimental contributions: (i) utilization of...

_e problem of outlier detection has been widely studied in existing literature because of its numerous applications in fraud detection,medical diagnostics, fault detection, and intrusion detection. A large category of outlier analysis algorithms have been proposed, such as proximity-based methods and local density-basedmethods. _esemethods are effective in ûnding outliers distributed along line...

Lepidopteran silks number in the thousands and display a vast diversity of structures, properties and industrial potential. To map this remarkable biochemical diversity, we present an identification and screening method based on the infrared spectra of native silk feedstock and cocoons. Multivariate analysis of over 1214 infrared spectra obtained from 35 species allowed us to group silks into d...

Investigating the extent (or the existence) of local adaptation is crucial to understanding how populations adapt. When experiments or fitness measurements are difficult or impossible to perform in natural populations, genomic techniques allow us to investigate local adaptation through the comparison of allele frequencies and outlier loci along environmental clines. The thick-billed murre (Uria...