Search results for "ComputingMethodologies_PATTERNRECOGNITION"

Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery

2013

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight cluster…

Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering

2017

Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on t…

2D motif basis applied to the classification of digital images

2016

The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. Different types of features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the set of candidate features. Recently, a special class of bidimensional motifs, i.e. 2D motif basis has been introduced in the literature. 2D motif basis showed to be powerful in capturing the r…

HyperLabelMe : A Web Platform for Benchmarking Remote-Sensing Image Classifiers

2017

HyperLabelMe is a web platform that allows the automatic benchmarking of remote-sensing image classifiers. To demonstrate this platform's attributes, we collected and harmonized a large data set of labeled multispectral and hyperspectral images with different numbers of classes, dimensionality, noise sources, and levels. The registered user can download training data pairs (spectra and land cover/use labels) and submit the predictions for unseen testing spectra. The system then evaluates the accuracy and robustness of the classifier, and it reports different scores as well as a ranked list of the best methods and users. The system is modular, scalable, and ever-growing in data sets and clas…

2014

This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy in fault detection. Therefore, there is no need to deal with original data or refer to other algorithms, making the classification problem simple to handle. In order to…

CellLineNavigator: a workbench for cancer cell line analysis

2012

The CellLineNavigator database, freely available at http://www.medicalgenomics.org/celllinenavigator, is a web-based workbench for large scale comparisons of a large collection of diverse cell lines. It aims to support experimental design in the fields of genomics, systems biology and translational biomedical research. Currently, this compendium holds genome wide expression profiles of 317 different cancer cell lines, categorized into 57 different pathological states and 28 individual tissues. To enlarge the scope of CellLineNavigator, the database was furthermore closely linked to commonly used bioinformatics databases and knowledge repositories. To ensure easy data access and search abili…

SEQPACKER: A Biologist-Friendly User Interface to Manipulate Nucleotide Sequences in Genomic Epidemiology

2004

The aim of this paper is to present a new integrated bioinformatics tool for manipulating nucleotide sequences with a user-friendly graphical interface. This tool is named “SeqPacker” because it uses DNA/RNA sequences. In addition, SeqPacker can be seen as a kind of nucleotide chain editor using standardized technologies, nucleotide representation standards, and high platform portability in support of research in Genomic Epidemiology. SeqPacker is written in JAVA as free and stand-alone software for several computer platforms.

Work-loss years among people diagnosed with diabetes: a reappraisal from a life course perspective

2018

Aims Early exit from the workforce has been proposed to be one of the unfavorable consequences of diabetes. We examined whether early exit from the workforce differed between persons who were and were not diagnosed with diabetes during their work career. Methods The cohort included 12,726 individuals of the Helsinki Birth Cohort Study, born between 1934 and 1944. Using data from nationwide registers, the cohort was followed up from early adulthood until they transitioned into retirement or died. Work- loss years were estimated using the restricted mean work years method. Results During a follow-up of 382,328 person-years for men and 349 894 for women, 36.8% transitioned into old age pension…

A structural cluster kernel for learning on graphs

2012

In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in…

The time course of processing handwritten words: An ERP investigation

2021

Available online 25 June 2021. Behavioral studies have shown that the legibility of handwritten script hinders visual word recognition. Furthermore, when compared with printed words, lexical effects (e.g., word-frequency effect) are magnified for less intelligible (difficult) handwriting (Barnhart and Goldinger, 2010; Perea et al., 2016). This boost has been interpreted in terms of greater influence of top-down mechanisms during visual word recognition. In the present experiment, we registered the participants’ ERPs to uncover top-down processing effects on early perceptual encoding. Participants’ behavioral and EEG responses were recorded to high- and low-frequency words that varied in scr…