scholarly journals Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
I. G. Damousis ◽  
S. Argyropoulos
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Support Vector ◽  
False Alarms ◽  
Security Systems ◽  
Vector Machines

We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support Vector Machines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

scholarly journals Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting

2018 ◽  
Vol 11 (3) ◽  
pp. 1121 ◽  
Author(s):  
Nor Azizah Hitam ◽  
Amelia Ritahani Ismail
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Small Range ◽  
Accuracy Rate ◽  
Comparative Performance ◽  
Vector Machines

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

LOCAL DESCRIPTOR MATCHING WITH SUPPORT VECTOR MACHINES

2010 ◽  
Vol 07 (01) ◽  
pp. 59-80
Author(s):  
D. CHENG ◽  
S. Q. XIE ◽  
E. HÄMMERLE
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Local Descriptor ◽  
Local Descriptors ◽  
Vector Machines ◽  
Three Stages ◽  
Image Transformations

Local descriptor matching is the most overlooked stage of the three stages of the local descriptor process, and this paper proposes a new method for matching local descriptors based on support vector machines. Results from experiments show that the developed method is more robust for matching local descriptors for all image transformations considered. The method is able to be integrated with different local descriptor methods, and with different machine learning algorithms and this shows that the approach is sufficiently robust and versatile.

scholarly journals FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

2017 ◽  
Vol IV-2/W4 ◽  
pp. 157-164 ◽  
Author(s):  
D. Wang ◽  
M. Hollaus ◽  
N. Pfeifer
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Learning Algorithms ◽  
Terrestrial Laser Scanning ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Area Index ◽  
Training Samples

Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an <i>Erytrophleum fordii</i> and a <i>Betula pendula</i> (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

scholarly journals Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part II

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1806
Author(s):  
Silvio Semanjski ◽  
Ivana Semanjski ◽  
Wim De Wilde ◽  
Sidharta Gautama
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Added Value ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Correlation Pattern ◽  
The Real

Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects: (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications.

scholarly journals Easyml: Easily Build and Evaluate Machine Learning Models

2017 ◽  
Author(s):  
Woo-Young Ahn ◽  
Paul Hendricks ◽  
Nathaniel Haines
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Linear Models ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Vector Machines ◽  
Barrier To Entry

AbstractThe easyml (easy machine learning) package lowers the barrier to entry to machine learning and is ideal for undergraduate/graduate students, and practitioners who want to quickly apply machine learning algorithms to their research without having to worry about the best practices of implementing each algorithm. The package provides standardized recipes for regression and classification algorithms in R and Python and implements them in a functional, modular, and extensible framework. This package currently implements recipes for several common machine learning algorithms (e.g., penalized linear models, random forests, and support vector machines) and provides a unified interface to each one. Importantly, users can run and evaluate each machine learning algorithm with a single line of coding. Each recipe is robust, implements best practices specific to each algorithm, and generates a report with details about the model, its performance, as well as journal-quality visualizations. The package’s functional, modular, and extensible framework also allows researchers and more advanced users to easily implement new recipes for other algorithms.

scholarly journals Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning

2019 ◽  
Vol 143 (8) ◽  
pp. 990-998 ◽  
Author(s):  
Min Yu ◽  
Lindsay A. L. Bazydlo ◽  
David E. Bruns ◽  
James H. Harrison
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Turnaround Time ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Test Dataset ◽  
Manual Review

Context.— Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results. Objective.— To determine whether a classification model created by using standard machine learning algorithms can verify analytically acceptable MS results and thereby reduce manual review requirements. Design.— We obtained retrospective data from gas chromatography–MS analyses of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH) in 1267 urine samples. The data for each sample had been labeled previously as either analytically unacceptable or acceptable by manual review. The dataset was randomly split into training and test sets (848 and 419 samples, respectively), maintaining equal proportions of acceptable (90%) and unacceptable (10%) results in each set. We used stratified 10-fold cross-validation in assessing the abilities of 6 supervised machine learning algorithms to distinguish unacceptable from acceptable assay results in the training dataset. The classifier with the highest recall was used to build a final model, and its performance was evaluated against the test dataset. Results.— In comparison testing of the 6 classifiers, a model based on the Support Vector Machines algorithm yielded the highest recall and acceptable precision. After optimization, this model correctly identified all unacceptable results in the test dataset (100% recall) with a precision of 81%. Conclusions.— Automated data review identified all analytically unacceptable assays in the test dataset, while reducing the manual review requirement by about 87%. This automation strategy can focus manual review only on assays likely to be problematic, allowing improved throughput and turnaround time without reducing quality.

The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. WB113-WB126 ◽  
Author(s):  
Matthew J. Cracknell ◽  
Anya M. Reading
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forests ◽  
Supervised Classification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Geophysical Data ◽  
Support Vector ◽  
Vector Machines ◽  
Spatially Varying

Inductive machine learning algorithms attempt to recognize patterns in, and generalize from empirical data. They provide a practical means of predicting lithology, or other spatially varying physical features, from multidimensional geophysical data sets. It is for this reason machine learning approaches are increasing in popularity for geophysical data inference. A key motivation for their use is the ease with which uncertainty measures can be estimated for nonprobabilistic algorithms. We have compared and evaluated the abilities of two nonprobabilistic machine learning algorithms, random forests (RF) and support vector machines (SVM), to recognize ambiguous supervised classification predictions using uncertainty calculated from estimates of class membership probabilities. We formulated a method to establish optimal uncertainty threshold values to identify and isolate the maximum number of incorrect predictions while preserving most of the correct classifications. This is illustrated using a case example of the supervised classification of surface lithologies in a folded, structurally complex, metamorphic terrain. We found that (1) the use of optimal uncertainty thresholds significantly improves overall classification accuracy of RF predictions, but not those of SVM, by eliminating the maximum number of incorrectly classified samples while preserving the maximum number of correctly classified samples; (2) RF, unlike SVM, was able to exploit dependencies and structures contained within spatially varying input data; and (3) high RF prediction uncertainty is spatially coincident with transitions in lithology and associated contact zones, and regions of intense deformation. Uncertainty has its upside in the identification of areas of key geologic interest and has wide application across the geosciences, where transition zones are important classes in their own right. The techniques used in this study are of practical value in prioritizing subsequent geologic field activities, which, with the aid of this analysis, may be focused on key lithology contacts and problematic localities.

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