scholarly journals Linear discriminant analysis of spatial Gaussian data with estimated anisotropy ratio

2011 ◽  
Vol 52 ◽  
Author(s):  
Lina Dreižienė
Keyword(s):  
Spatial Data ◽  
Asymptotic Approximation ◽  
Covariance Function ◽  
Anisotropy Ratio ◽  
Linear Discriminant ◽  
Gaussian Data ◽  
Unknown Covariance ◽  
Two Populations ◽  
Bayes Discriminant Function

The paper deals with a problem of classification of Gaussian spatial data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function. In the case of an unknown mean and covariance parameters the Plug-in Bayes discriminant function based on ML estimators is used. The asymptotic approximation of expected error rate (AER) is derived in the case of unknown mean parameters and single unknown covariance parameter i.e., anisotropy ratio.  

scholarly journals The influence of training sampling size on the expected error rate in spatial classification

2012 ◽  
Vol 53 ◽  
Author(s):  
Lina Dreižienė ◽  
Marta Karaliutė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Asymptotic Approximation ◽  
Covariance Function ◽  
Spatial Sampling ◽  
Anisotropy Ratio ◽  
Gaussian Data ◽  
Two Populations ◽  
Bayes Discriminant Function

In this paper we use the pluged-in Bayes discriminant function (PBDF) for classification of spatial Gaussian data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function. The pluged-in Bayes discriminant function is constructed by using ML estimators of unknown mean and anisotropy ratio parameters. We focus on the asymptotic approximation of expected error rate (AER) and our aim is to investigate the effects of two different spatial sampling designs (based on increasing and fixed domain asymptotics) on AER.

scholarly journals Effect of anisotropy coeficient on error rates of linear discriminant functions

2021 ◽  
Vol 47 ◽  
Author(s):  
Kęstutis Dučinskas ◽  
Lina Dreižienė
Keyword(s):  
Spatial Data ◽  
Supervised Classification ◽  
Statistical Approach ◽  
Gaussian Random Field ◽  
Error Rates ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Recognition Error ◽  
Two Populations

Paper deals with statistical classification of spatial data as a part of widely applicable statistical approach to pattern recognition. Error rates in supervised classification of Gaussian random field observation into one of two populations specified by different constant means and common stationary geometric anisotropic covariance are considered. Formula for the exact Bayesian error rate is derived. The influence of the ratio of anisotropy to the error rates is evaluated numerically for the case of complete parametric certainty.

scholarly journals Application of spatial auto-beta models in statistical classification

2021 ◽  
Vol 62 ◽  
pp. 36-43
Author(s):  
Eglė Zikarienė ◽  
Kęstutis Dučinskas
Keyword(s):  
Discriminant Function ◽  
Spatial Data ◽  
Supervised Classification ◽  
Classification Problem ◽  
Error Rates ◽  
Classification Rules ◽  
Linear Discriminant ◽  
Actual Error ◽  
Two Populations ◽  
Bayes Discriminant Function

In this paper, spatial data specified by auto-beta models is analysed by considering a supervised classification problem of classifying feature observation into one of two populations. Two classification rules based on conditional Bayes discriminant function (BDF) and linear discriminant function (LDF) are proposed. These classification rules are critically compared by the values of the actual error rates through the simulation study.

Paepalanthus fabianeae (Eriocaulaceae): A New Microendemic Species from a Morphologically Coherent Clade

2020 ◽  
Vol 45 (4) ◽  
pp. 794-801
Author(s):  
Caroline Oliveira Andrino ◽  
Marcelo Fragomeni Simon ◽  
Jair Eustáquio Quintino Faria ◽  
André Luiz da Costa Moreira ◽  
Paulo Takeo Sano
Keyword(s):  
New Species ◽  
Iucn Red List ◽  
Phylogenetic Position ◽  
Similar Species ◽  
Infrageneric Classification ◽  
Espinhaço Range ◽  
Nuclear Its ◽  
Two Populations

Abstract—We describe and illustrate Paepalanthus fabianeae, a new species of Eriocaulaceae from the central portion of the Espinhaço Range in Minas Gerais, Brazil. Previous phylogenetic evidence based on analyses of nuclear (ITS and ETS) and plastid (trnL-trnF and psba-trnH) sequences revealed P. fabianeae as belonging to a strongly supported and morphologically coherent clade containing five other species, all of them microendemic, restricted to the Espinhaço range. Due to the infrageneric classification of Paepalanthus being highly artificial, we preferred not assigning P. fabianeae to any infrageneric group. Paepalanthus fabianeae is known from two populations growing in campos rupestres (highland rocky fields) in the meridional Espinhaço Range. The species is characterized by pseudodichotomously branched stems, small, linear, recurved, and reflexed leaves, urceolate capitula, and bifid stigmas. Illustrations, photos, the phylogenetic position, and a detailed description, as well as comments on habitat, morphology, and affinities with similar species are provided. The restricted area of occurrence allied with threats to the quality of the habitat, mainly due to quartzite mining, justifies the preliminary classification of the new species in the Critically Endangered (CR) category using the guidelines and criteria of the IUCN Red List.

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

scholarly journals Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2547 ◽  
Author(s):  
Tuo Gao ◽  
Yongchen Wang ◽  
Chengwu Zhang ◽  
Zachariah A. Pittman ◽  
Alexandra M. Oliveira ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Single Type ◽  
Multivariate Techniques ◽  
Integrated Sensor ◽  
Resistance Change ◽  
Linear Discriminant ◽  
Lift Off

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R0), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

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