Investigating the performance of the supervised learning algorithms for estimating NPPs parameters in combination with the different feature selection techniques

2021 ◽  
Vol 158 ◽  
pp. 108299
Author(s):  
Khalil Moshkbar-Bakhshayesh
Keyword(s):  
Feature Selection ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Supervised Learning Algorithms ◽  
Feature Selection Techniques

Supervised Learning-Based Feature Selection for Mondrian Paintings Style Authentication

2012 ◽  
Vol 16 (7) ◽  
pp. 894-899 ◽  
Author(s):  
Keon Myung Lee ◽  
◽  
Kyoung Soon Hwang ◽  
Kyung Mi Lee ◽  
Seung Kee Han ◽  
...  
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Supervised Learning ◽  
Computational Analysis ◽  
Learning Algorithms ◽  
Forgery Detection ◽  
Decision Tree Induction ◽  
Work Style ◽  
Supervised Learning Algorithms ◽  
Selection For

This paper concerns feature selection for computational analysis in authenticating works of art. The various features designed and extracted from art work in art forgery detection or the identification of the characteristics of art work style are valuable only when they have a meaningful influence on a given task such as classification. This paper presents features applicable to authenticating the painting style of Piet Mondrian and demonstrates meaningful features by using two supervised learning algorithms, a decision tree induction algorithm C4.5 and the Feature Generating Machine (FGM), both of which are used to select important features in the course of learning.

Supervised learning algorithms in the classification of plant populations with different degrees of kinship

2021 ◽  
Author(s):  
Leandro Skowronski ◽  
Paula Martin de Moraes ◽  
Mario Luiz Teixeira de Moraes ◽  
Wesley Nunes Gonçalves ◽  
Michel Constantino ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Plant Populations ◽  
Supervised Learning Algorithms

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 139 ◽  
Author(s):  
Ioannis Livieris ◽  
Andreas Kanavos ◽  
Vassilis Tampakas ◽  
Panagiotis Pintelas
Keyword(s):  
Supervised Learning ◽  
Predictive Models ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Classification Problem ◽  
Classification Methods ◽  
Training Algorithm ◽  
Traditional Classification ◽  
Supervised Learning Algorithms ◽  
Significant Research

Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

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