A new approach to the species classification problem in floristic analysis

1982 ◽  
Vol 7 (1) ◽  
pp. 75-89 ◽  
Author(s):  
M. P. AUSTIN ◽  
L. BELBIN
Keyword(s):  
Classification Problem ◽  
Species Classification ◽  
New Approach ◽  
Floristic Analysis

scholarly journals Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 425
Author(s):  
Krzysztof Gajowniczek ◽  
Iga Grzegorczyk ◽  
Michał Gostkowski ◽  
Tomasz Ząbkowski
Keyword(s):  
Blind Source Separation ◽  
Cardiac Arrhythmias ◽  
Classification Accuracy ◽  
Recursive Partitioning ◽  
Source Separation ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Physiological Signals ◽  
New Approach ◽  
Model Aggregation

In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

scholarly journals A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

scholarly journals Machine learning techniques for jet flavour identification at CMS

2019 ◽  
Vol 214 ◽  
pp. 06010
Author(s):  
Mauro Verzetti
Keyword(s):  
Classification Problem ◽  
Machine Learning Techniques ◽  
High Momentum ◽  
New Approach ◽  
Physics Program ◽  
Cms Experiment ◽  
Standard Operation ◽  
Learning Techniques ◽  
Multi Classification ◽  
Individual Particles

Jet flavour identification is a fundamental component of the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. We present results from a realistic simulation of the CMS detector, one of two multi-purpose detectors at the LHC, and the respective performance measured on data. Our tagger, named DeepJet, relies heavily on applying convolutions on lower level physics objects, like individual particles. This approach allows the usage of an unprecedented amount of information with respect to what is found in the literature. DeepJet stands out as the first proposal that can be applied to multi-classification for all jet flavours. We demonstrate significant improvements by the new approach on the classification capabilities of the CMS experiment in simulation in several of the tested classes. At high momentum improvements of nearly 90% less false positives at a standard operation point are reached.

GENETIC PROGRAMMING APPROACH FOR MULTI-CATEGORY PATTERN CLASSIFICATION APPLIED TO NETWORK INTRUSIONS DETECTION

2006 ◽  
Vol 06 (01) ◽  
pp. 77-99 ◽  
Author(s):  
K. M. FARAOUN ◽  
A. BOUKELIF
Keyword(s):  
Genetic Programming ◽  
Classification Problem ◽  
Programming Approach ◽  
Genetic Approach ◽  
Nonlinear Transformations ◽  
New Approach ◽  
Network Intrusions ◽  
Reduced Dimension ◽  
New Space ◽  
Fitness Criterion

The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.

scholarly journals An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation

2017 ◽  
Vol 12 (06) ◽  
pp. 161
Author(s):  
Yuehua Gao ◽  
Tianyi Chen
Keyword(s):  
Extreme Learning Machine ◽  
Classification Problem ◽  
Fuzzy Information ◽  
New Approach ◽  
Generalization Ability ◽  
Information Granulation ◽  
Learning Efficiency ◽  
Learning Machine ◽  
Hidden Nodes ◽  
Fuzzy Information Granulation

In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. The new approach not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed values. The experimental results show that the proposed method is effective and can produce desirable generalization performance in most cases based on a few regression and classification problem.

scholarly journals Conditionally optimal classification based on CFAR and invariance property for blind receivers

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Masoud Naderpour ◽  
Hossein Khaleghi Bizaki
Keyword(s):  
Error Probability ◽  
Optimal Solution ◽  
Classification Problem ◽  
Invariance Property ◽  
Modulation Classification ◽  
Constant False Alarm Rate ◽  
New Approach ◽  
Minimum Error ◽  
Blind Receivers ◽  
Uniformly Most Powerful

AbstractThis paper proposes a new approach for finding the conditionally optimal solution (the classifier with minimum error probability) for the classification problem where the observations are from the multivariate normal distribution. The optimal Bayes classifier does not exist when the covariance matrix is unknown for this problem. However, this paper proposes a classifier based on the constant false alarm rate (CFAR) and invariance property. The proposed classifier is optimal conditionally as it has the minimum error probability in a subset of solutions. This approach has an analogy to hypothesis testing problems where uniformly most powerful invariant (UMPI) and uniformly most powerful unbiased (UMPU) detectors are used instead of the non-existing optimal UMP detector. Furthermore, this paper investigates using the proposed classifier for modulation classification as an application in signal processing.

scholarly journals New Hyperspectral Procedure to Discriminate Intertidal Macroalgae

2022 ◽  
Vol 14 (2) ◽  
pp. 346
Author(s):  
Florian Douay ◽  
Charles Verpoorter ◽  
Gwendoline Duong ◽  
Nicolas Spilmont ◽  
François Gevaert
Keyword(s):  
Spectral Response ◽  
Hyperspectral Data ◽  
Species Level ◽  
Embedded Sensors ◽  
Viewing Angle ◽  
Species Classification ◽  
Spectral Angle Mapper ◽  
New Approach ◽  
Classification Approach ◽  
First Derivative

The recent development and miniaturization of hyperspectral sensors embedded in drones has allowed the acquisition of hyperspectral images with high spectral and spatial resolution. The characteristics of both the embedded sensors and drones (viewing angle, flying altitude, resolution) create opportunities to consider the use of hyperspectral imagery to map and monitor macroalgae communities. In general, the overflight of the areas to be mapped is conconmittently associated accompanied with measurements carried out in the field to acquire the spectra of previously identified objects. An alternative to these simultaneous acquisitions is to use a hyperspectral library made up of pure spectra of the different species in place, that would spare field acquisition of spectra during each flight. However, the use of such a technique requires developed appropriate procedure for testing the level of species classification that can be achieved, as well as the reproducibility of the classification over time. This study presents a novel classification approach based on the use of reflectance spectra of macroalgae acquired in controlled conditions. This overall approach developed is based on both the use of the spectral angle mapper (SAM) algorithm applied on first derivative hyperspectral data. The efficiency of this approach has been tested on a hyperspectral library composed of 16 macroalgae species, and its temporal reproducibility has been tested on a monthly survey of the spectral response of different macro-algae species. In addition, the classification results obtained with this new approach were also compared to the results obtained through the use of the most recent and robust procedure published. The classification obtained shows that the developed approach allows to perfectly discriminate the different phyla, whatever the period. At the species level, the classification approach is less effective when the individuals studied belong to phylogenetically close species (i.e., Fucus spiralis and Fucus serratus).

scholarly journals Interactive Optimal Teaching with Unknown Learners

2018 ◽  
Author(s):  
Francisco S. Melo ◽  
Carla Guerra ◽  
Manuel Lopes
Keyword(s):  
Learning Process ◽  
Learning Algorithm ◽  
Classification Problem ◽  
Actual Process ◽  
Worst Case ◽  
New Approach ◽  
Linear Discriminant ◽  
Imperfect Knowledge ◽  
The Impact ◽  
Lack Of Knowledge

This paper introduces a new approach for machine teaching that partly addresses the (unavoidable) mismatch between what the teacher assumes about the learning process of the student and the actual process. We analyze several situations in which such mismatch takes place, including when the student?s learning algorithm is known but the corresponding parameters are not, and when the learning algorithm itself is not known. Our analysis is focused on the case of a Bayesian Gaussian learner, and we show that, even in this simple case, the lack of knowledge regarding the student?s learning process significantly deteriorates the performance of machine teaching: while perfect knowledge of the student ensures that the target is learned after a finite number of samples, lack of knowledge thereof implies that the student will only learn asymptotically (i.e., after an infinite number of samples). We introduce interactivity as a means to mitigate the impact of imperfect knowledge and show that, by using interactivity, we are able to recover finite learning time, in the best case, or significantly faster convergence, in the worst case. Finally, we discuss the extension of our analysis to a classification problem using linear discriminant analysis, and discuss the implications of our results in single- and multi-student settings.

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