113,662 research outputs found
Fast Selection of Spectral Variables with B-Spline Compression
The large number of spectral variables in most data sets encountered in
spectral chemometrics often renders the prediction of a dependent variable
uneasy. The number of variables hopefully can be reduced, by using either
projection techniques or selection methods; the latter allow for the
interpretation of the selected variables. Since the optimal approach of testing
all possible subsets of variables with the prediction model is intractable, an
incremental selection approach using a nonparametric statistics is a good
option, as it avoids the computationally intensive use of the model itself. It
has two drawbacks however: the number of groups of variables to test is still
huge, and colinearities can make the results unstable. To overcome these
limitations, this paper presents a method to select groups of spectral
variables. It consists in a forward-backward procedure applied to the
coefficients of a B-Spline representation of the spectra. The criterion used in
the forward-backward procedure is the mutual information, allowing to find
nonlinear dependencies between variables, on the contrary of the generally used
correlation. The spline representation is used to get interpretability of the
results, as groups of consecutive spectral variables will be selected. The
experiments conducted on NIR spectra from fescue grass and diesel fuels show
that the method provides clearly identified groups of selected variables,
making interpretation easy, while keeping a low computational load. The
prediction performances obtained using the selected coefficients are higher
than those obtained by the same method applied directly to the original
variables and similar to those obtained using traditional models, although
using significantly less spectral variables
A Rank Minrelation - Majrelation Coefficient
Improving the detection of relevant variables using a new bivariate measure
could importantly impact variable selection and large network inference
methods. In this paper, we propose a new statistical coefficient that we call
the rank minrelation coefficient. We define a minrelation of X to Y (or
equivalently a majrelation of Y to X) as a measure that estimate p(Y > X) when
X and Y are continuous random variables. The approach is similar to Lin's
concordance coefficient that rather focuses on estimating p(X = Y). In other
words, if a variable X exhibits a minrelation to Y then, as X increases, Y is
likely to increases too. However, on the contrary to concordance or
correlation, the minrelation is not symmetric. More explicitly, if X decreases,
little can be said on Y values (except that the uncertainty on Y actually
increases). In this paper, we formally define this new kind of bivariate
dependencies and propose a new statistical coefficient in order to detect those
dependencies. We show through several key examples that this new coefficient
has many interesting properties in order to select relevant variables, in
particular when compared to correlation
Spatial filters selection towards a rehabilitation BCI
Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step.
Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training.
Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context
Software defect prediction: do different classifiers find the same defects?
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio
The development of service provider's BPO-IT framework
Purpose
The decision to operate BPO-IT organisational model by a business process outsourcing (BPO) service provider has far reaching benefits. The purpose of this paper is to develop a service provider’s BPO-IT framework that provides in-house IT function (software) required to process client services.
Design/methodology/approach
The multi-case study adopted an exploratory sequential mixed method research approach. In the first instance, seven BPO service provider organisations were investigated in the qualitative phase and 156 in the quantitative phase, respectively.
Findings
The adoption of the developed framework indicates that it could reduce failures in BPO relationships through reduced turnaround time in processing client services, improved quality of service, reduced cost, improved client and provider’s competitiveness, and confidentiality of client operations. Outsourcing clients could lay the foundation for a successful relationship by adopting a selection process that could choose the right provider.
Originality/value
The paper reveals BPO-IT organisation’s operation towards in-house provision of software required to process client services. A research exploring BPO service providers from a top outsourcing destination like India could provide offshore outsourcing clients the information to move towards onshore outsourcing.
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