SUPPORT VECTOR MACHINE TO ESTIMATE VOLUME OF EUCALYPT TREES

ABSTRACT This study aimed to test the application of the technique of support vector machines (SVM) to estimate the volume of eucalyptus trees. The data used in this study were from of 2307 trees of clonal hybrids (Eucalyptus grandis x Eucalyptus urophylla) located in southern Bahia. In the definition of stratification traditionally used 53 stratums were defined (defined by the stratification project and clone). He set the model of Schumacher and Hall for each stratum. The SVM were constructed to correlate the volume of trees on the basis of other independent variables which may be numeric as dbh and height and categorical as genetic material and design. The estimates were analyzed using statistical and graphical analysis of residues. The analysis consisted of the graphical inspection statistical dispersion of errors (residuals) in relation to the percentage of the values observed, and the analysis of the histogram of residues. The statistics used were the correlation between the observed and estimated volumes. The model of Schumacher and Hall showed the correlation between observed and predicted values of 0,993, and the SVM set of correlated 0,994. The SVM technology showed good adaptation to the problem, and this can use to predict the volumetric production of planted forests.

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Classification of Oncologic Data with Genetic Programming

Journal of Artificial Evolution and Applications ◽

10.1155/2009/848532 ◽

2009 ◽

Vol 2009 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Leonardo Vanneschi ◽

Francesco Archetti ◽

Mauro Castelli ◽

Ilaria Giordani

Keyword(s):

Machine Learning ◽

Genetic Programming ◽

Lymphoblastic Leukemia ◽

Genetic Material ◽

Support Vector ◽

Biomedical Data ◽

Large Sets ◽

Vector Machines ◽

Microarray Technique

Discovering the models explaining the hidden relationship between genetic material and tumor pathologies is one of the most important open challenges in biology and medicine. Given the large amount of data made available by the DNA Microarray technique, Machine Learning is becoming a popular tool for this kind of investigations. In the last few years, we have been particularly involved in the study of Genetic Programming for mining large sets of biomedical data. In this paper, we present a comparison between four variants of Genetic Programming for the classification of two different oncologic datasets: the first one contains data from healthy colon tissues and colon tissues affected by cancer; the second one contains data from patients affected by two kinds of leukemia (acute myeloid leukemia and acute lymphoblastic leukemia). We report experimental results obtained using two different fitness criteria: the receiver operating characteristic and the percentage of correctly classified instances. These results, and their comparison with the ones obtained by three nonevolutionary Machine Learning methods (Support Vector Machines, MultiBoosting, and Random Forests) on the same data, seem to hint that Genetic Programming is a promising technique for this kind of classification.

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Network Attack Classification in IoT Using Support Vector Machines

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10030058 ◽

2021 ◽

Vol 10 (3) ◽

pp. 58

Author(s):

Christiana Ioannou ◽

Vasos Vassiliou

Keyword(s):

Support Vector Machines ◽

Computer Network ◽

Detection System ◽

Network Activity ◽

Support Vector ◽

Network Attack ◽

Vector Machines ◽

Normal Network ◽

Definition Of ◽

Suspicious Activity

Machine learning (ML) techniques learn a system by observing it. Events and occurrences in the network define what is expected of the network’s operation. It is for this reason that ML techniques are used in the computer network security field to detect unauthorized intervention. In the event of suspicious activity, the result of the ML analysis deviates from the definition of expected normal network activity and the suspicious activity becomes apparent. Support vector machines (SVM) are ML techniques that have been used to profile normal network activity and classify it as normal or abnormal. They are trained to configure an optimal hyperplane that classifies unknown input vectors’ values based on their positioning on the plane. We propose to use SVM models to detect malicious behavior within low-power, low-rate and short range networks, such as those used in the Internet of Things (IoT). We evaluated two SVM approaches, the C-SVM and the OC-SVM, where the former requires two classes of vector values (one for the normal and one for the abnormal activity) and the latter observes only normal behavior activity. Both approaches were used as part of an intrusion detection system (IDS) that monitors and detects abnormal activity within the smart node device. Actual network traffic with specific network-layer attacks implemented by us was used to create and evaluate the SVM detection models. It is shown that the C-SVM achieves up to 100% classification accuracy when evaluated with unknown data taken from the same network topology it was trained with and 81% accuracy when operating in an unknown topology. The OC-SVM that is created using benign activity achieves at most 58% accuracy.

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Learning to Generate Optimized Term Weighting for Web Documents Classification - A Parallel Mimetic Approach Based on Support Vector Machines

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v11i12.10964 ◽

2016 ◽

Vol 11 (12) ◽

pp. 1147

Author(s):

Abderrahmane Bendahmane ◽

Abdelkader Benyettou

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

Term Weighting ◽

Web Documents ◽

Vector Machines

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A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

10.26434/chemrxiv.11526132 ◽

2020 ◽

Author(s):

Lewis Mervin ◽

Avid M. Afzal ◽

Ola Engkvist ◽

Andreas Bender

Keyword(s):

Target Prediction ◽

Support Vector ◽

Machine Learning Method ◽

Learning Method ◽

Protein Target ◽

Bioactivity Prediction ◽

Vector Machines ◽

Scaling Methods ◽

Data Points ◽

Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

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Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1770 ◽

2020 ◽

Vol 4 (2) ◽

pp. 329-335

Author(s):

Rusydi Umar ◽

Imam Riadi ◽

Purwono

Keyword(s):

Social Media ◽

Gradient Descent ◽

Classification Model ◽

Stochastic Gradient Descent ◽

Support Vector ◽

Svm Algorithm ◽

Vector Machines ◽

Performance Patterns ◽

A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

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