Ensemblator: An ensemble of classifiers for reliable classification of biological data

2007 ◽  
Vol 28 (5) ◽  
pp. 622-630 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini
Keyword(s):  
Biological Data ◽  
Ensemble Of Classifiers ◽  
Reliable Classification

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals An Automated Method for the Quantification and Fractal Analysis of Immunostaining

2004 ◽  
Vol 26 (3) ◽  
pp. 125-134
Author(s):  
Armin Gerger ◽  
Patrick Bergthaler ◽  
Josef Smolle
Keyword(s):  
Fractal Analysis ◽  
Quantitative Description ◽  
Fractal Dimensionality ◽  
Texture Features ◽  
Second Step ◽  
Digital Information ◽  
Automated Method ◽  
Reliable Classification ◽  
The One

Aims. In tissue counter analysis (TCA) digital images of complex histologic sections are dissected into elements of equal size and shape, and digital information comprising grey level, colour and texture features is calculated for each element. In this study we assessed the feasibility of TCA for the quantitative description of amount and also of distribution of immunostained material. Methods. In a first step, our system was trained for differentiating between background and tissue on the one hand and between immunopositive and so‐called other tissue on the other. In a second step, immunostained slides were automatically screened and the procedure was tested for the quantitative description of amount of cytokeratin (CK) and leukocyte common antigen (LCA) immunopositive structures. Additionally, fractal analysis was applied to all cases describing the architectural distribution of immunostained material. Results. The procedure yielded reproducible assessments of the relative amounts of immunopositive tissue components when the number and percentage of CK and LCA stained structures was assessed. Furthermore, a reliable classification of immunopositive patterns was found by means of fractal dimensionality. Conclusions. Tissue counter analysis combined with classification trees and fractal analysis is a fully automated and reproducible approach for the quantitative description in immunohistology.

scholarly journals Optimized artificial neural network for classification of biological data

2018 ◽  
Vol 7 (2) ◽  
pp. 817
Author(s):  
Senthilselvan Natarajan ◽  
Rajarajan S ◽  
Subramaniyaswamy V
Keyword(s):  
Neural Network ◽  
Mental Stress ◽  
Causes Of Death ◽  
Biological Data ◽  
High Dimensionality ◽  
Soft Computing Techniques ◽  
Artificial Neural ◽  
Multi Class Classification ◽  
Neural Networks Optimization

Biological data suffers from the problem of high dimensionality which makes the process of multi-class classification difficult and also these data have elements that are incomplete and redundant. Breast Cancer is currently one of the most pre-dominant causes of death in women around the globe. The current methods for classifying a tumour as malignant or benign involve physical procedures. This often leads to mental stress. Research has now sought to implement soft computing techniques in order to classify tumours based on the data available. In this paper, a novel classifier model is implemented using Artificial Neural Networks. Optimization is done in this neural network by using a meta-heuristic algorithm called the Whale Swarm Algorithm in order to make the classifier model accurate. Experimental results show that new technique outperforms other existing models.

Classification of Biological Sequences

Data Mining ◽  
2013 ◽  
pp. 1019-1042
Author(s):  
Pratibha Rani ◽  
Vikram Pudi
Keyword(s):  
Naive Bayes ◽  
Analysis Data ◽  
Naïve Bayes ◽  
Biological Data ◽  
Rapid Progress ◽  
Biological Data Analysis ◽  
Depth Analysis ◽  
Iterative Scaling ◽  
Mining Methods

The rapid progress of computational biology, biotechnology, and bioinformatics in the last two decades has led to the accumulation of tremendous amounts of biological data that demands in-depth analysis. Data mining methods have been applied successfully for analyzing this data. An important problem in biological data analysis is to classify a newly discovered sequence like a protein or DNA sequence based on their important features and functions, using the collection of available sequences. In this chapter, we study this problem and present two Bayesian classifiers RBNBC (Rani & Pudi, 2008a) and REBMEC (Rani & Pudi, 2008c). The algorithms used in these classifiers incorporate repeated occurrences of subsequences within each sequence (Rani, 2008). Specifically, Repeat Based Naive Bayes Classifier (RBNBC) uses a novel formulation of Naive Bayes, and the second classifier, Repeat Based Maximum Entropy Classifier (REBMEC) uses a novel framework based on the classical Generalized Iterative Scaling (GIS) algorithm.

scholarly journals Alignment and Proficiency of Virgin Olive Oil Sensory Panels: The OLEUM Approach

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 355 ◽  
Author(s):  
Sara Barbieri ◽  
Karolina Brkić Bubola ◽  
Alessandra Bendini ◽  
Milena Bučar-Miklavčič ◽  
Florence Lacoste ◽  
...  
Keyword(s):  
Quality Control ◽  
Decision Tree ◽  
Olive Oil ◽  
Reference Materials ◽  
Virgin Olive Oil ◽  
Sensory Data ◽  
Physical Chemical ◽  
Control Procedures ◽  
Reliable Classification

A set of 334 commercial virgin olive oil (VOO) samples were evaluated by six sensory panels during the H2020 OLEUM project. Sensory data were elaborated with two main objectives: (i) to classify and characterize samples in order to use them for possible correlations with physical–chemical data and (ii) to monitor and improve the performance of panels. After revision of the IOC guidelines in 2018, this work represents the first published attempt to verify some of the recommended quality control tools to increase harmonization among panels. Specifically, a new “decision tree” scheme was developed, and some IOC quality control procedures were applied. The adoption of these tools allowed for reliable classification of 289 of 334 VOOs; for the remaining 45, misalignments between panels of first (on the category, 21 cases) or second type (on the main perceived defect, 24 cases) occurred. In these cases, a “formative reassessment” was necessary. At the end, 329 of 334 VOOs (98.5%) were classified, thus confirming the effectiveness of this approach to achieve a better proficiency. The panels showed good performance, but the need to adopt new reference materials that are stable and reproducible to improve the panel’s skills and agreement also emerged.

scholarly journals Clumpiness: time-domain classification of red giant evolutionary states

2020 ◽  
Vol 497 (4) ◽  
pp. 4843-4856 ◽  
Author(s):  
James S Kuszlewicz ◽  
Saskia Hekker ◽  
Keaton J Bell
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Red Giants ◽  
Distance Information ◽  
Evolutionary State ◽  
State Classification ◽  
Red Giant ◽  
Reliable Classification

ABSTRACT Long, high-quality time-series data provided by previous space missions such as CoRoT and Kepler have made it possible to derive the evolutionary state of red giant stars, i.e. whether the stars are hydrogen-shell burning around an inert helium core or helium-core burning, from their individual oscillation modes. We utilize data from the Kepler mission to develop a tool to classify the evolutionary state for the large number of stars being observed in the current era of K2, TESS, and for the future PLATO mission. These missions provide new challenges for evolutionary state classification given the large number of stars being observed and the shorter observing duration of the data. We propose a new method, Clumpiness, based upon a supervised classification scheme that uses ‘summary statistics’ of the time series, combined with distance information from the Gaia mission to predict the evolutionary state. Applying this to red giants in the APOKASC catalogue, we obtain a classification accuracy of $\sim 91{{\ \rm per\ cent}}$ for the full 4 yr of Kepler data, for those stars that are either only hydrogen-shell burning or also helium-core burning. We also applied the method to shorter Kepler data sets, mimicking CoRoT, K2, and TESS achieving an accuracy $\gt 91{{\ \rm per\ cent}}$ even for the 27 d time series. This work paves the way towards fast, reliable classification of vast amounts of relatively short-time-span data with a few, well-engineered features.

scholarly journals Family-group names proposed in the family Pseudococcidae (Hemiptera: Sternorrhyncha: Coccoidea)

Zootaxa ◽  
2010 ◽  
Vol 2400 (1) ◽  
pp. 66 ◽  
Author(s):  
D. J. WILLIAMS ◽  
P. J. GULLAN
Keyword(s):  
Family Group ◽  
The Family ◽  
Family Level ◽  
The Status ◽  
Reliable Classification

Since Cockerell (1905) erected the family-group name Pseudococcini, the name has become widely used for all mealybugs. Lobdell (1930) raised the status of the group to family level as the Pseudococcidae, but it was not until Borchsenius (1949) and Ferris (1950) accepted the family level that the rank of Pseudococcidae became more widely accepted within the superfamily Coccoidea. Various tribes and subtribes have been introduced without any reliable classification of the family.

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