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.

scholarly journals Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

2018 ◽  
Vol 10 (11) ◽  
pp. 1675 ◽  
Author(s):  
Devin Routh ◽  
Lindsi Seegmiller ◽  
Charlie Bettigole ◽  
Catherine Kuhn ◽  
Chadwick D. Oliver ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Cross Validation ◽  
Hyperspectral Image ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Hyperspectral Data ◽  
Matched Filtering ◽  
User Input ◽  
Remote Sensing Classification ◽  
Supervised Learning Algorithms

Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration’s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications’ underlying efficacies.

scholarly journals Identification Of Hepatocellular Carcinoma Using Supervised Learning Algorithms

2014 ◽  
Vol 14 (3) ◽  
pp. 5535-5542
Author(s):  
Sagri Sharma ◽  
Sanjay Kadam ◽  
Hemant Darbari
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Supervised Learning ◽  
Dna Analysis ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Learning Algorithms ◽  
Support Vector ◽  
Unseen Data ◽  
Supervised Learning Algorithms

Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms & statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data

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.

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