Influence of Fractal Dimension on Network Anomalies Binary Classification Quality Using Machine Learning Methods

2020 ◽  
Vol 54 (3) ◽  
pp. 216-228
Author(s):  
O. I. Sheluhin ◽  
M. A. Kazhemskiy
Keyword(s):  
Machine Learning ◽  
Fractal Dimension ◽  
Binary Classification ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Classification Quality ◽  
Network Anomalies

Prediction of Compound-Protein Interactions with Machine Learning Methods

2011 ◽  
pp. 304-317
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

scholarly journals Rescale-Invariant SVM for Binary Classification

2017 ◽  
Author(s):  
Mojtaba Montazery ◽  
Nic Wilson
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Experimental Results ◽  
Support Vector ◽  
Computation Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

Support Vector Machines (SVM) are among the most well-known machine learning methods, with broad use in different scientific areas. However, one necessary pre-processing phase for SVM is normalization (scaling) of features, since SVM is not invariant to the scales of the features’ spaces, i.e., different ways of scaling may lead to different results. We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that class for all possible rescalings of features. We derive a way of characterising the approach for binary SVM that allows determining when an instance strongly belongs to a class and when the classification is invariant to rescaling. The characterisation leads to a computation method to determine whether one sample is strongly positive, strongly negative or neither. Our experimental results back up the intuition that being strongly positive suggests stronger confidence that an instance really is positive.

Prediction of Compound-protein Interactions with Machine Learning Methods

2012 ◽  
pp. 616-630
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

scholarly journals Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3085 ◽  
Author(s):  
Raluca Brehar ◽  
Delia-Alexandrina Mitrea ◽  
Flaviu Vancea ◽  
Tiberiu Marita ◽  
Sergiu Nedevschi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Binary Classification ◽  
Classification Performance ◽  
Ultrasound Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Segmentation Accuracy ◽  
Conventional Machine

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.

Ensemble machine learning methods for spatio-temporal data analysis of plant and ratoon sugarcane

2021 ◽  
Vol 25 (5) ◽  
pp. 1291-1322
Author(s):  
Sandeep Kumar Singla ◽  
Rahul Dev Garg ◽  
Om Prakash Dubey
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Binary Classification ◽  
Temporal Variations ◽  
Classification Model ◽  
Gradient Boosting ◽  
Remotely Sensed Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Classification And Regression

Recent technological enhancements in the field of information technology and statistical techniques allowed the sophisticated and reliable analysis based on machine learning methods. A number of machine learning data analytical tools may be exploited for the classification and regression problems. These tools and techniques can be effectively used for the highly data-intensive operations such as agricultural and meteorological applications, bioinformatics and stock market analysis based on the daily prices of the market. Machine learning ensemble methods such as Decision Tree (C5.0), Classification and Regression (CART), Gradient Boosting Machine (GBM) and Random Forest (RF) has been investigated in the proposed work. The proposed work demonstrates that temporal variations in the spectral data and computational efficiency of machine learning methods may be effectively used for the discrimination of types of sugarcane. The discrimination has been considered as a binary classification problem to segregate ratoon from plantation sugarcane. Variable importance selection based on Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) have been used to create the appropriate dataset for the classification. The performance of the binary classification model based on RF is the best in all the possible combination of input images. Feature selection based on MDA and MDG measures of RF is also important for the dimensionality reduction. It has been observed that RF model performed best with 97% accuracy, whereas the performance of GBM method is the lowest. Binary classification based on the remotely sensed data can be effectively handled using random forest method.

scholarly journals In silico prediction of serious eye irritation or corrosion potential of chemicals

RSC Advances ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. 6697-6703 ◽  
Author(s):  
Qin Wang ◽  
Xiao Li ◽  
Hongbin Yang ◽  
Yingchun Cai ◽  
Yinyin Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Corrosion Potential ◽  
Binary Classification ◽  
Classification Models ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Eye Irritation ◽  
Machine Learning Methods ◽  
Chemical Fingerprints

Chemical fingerprints combined with machine learning methods were used to build binary classification models for predicting the potential EC/EI of compounds.

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