A reduced universum twin support vector machine for class imbalance learning
In most of the real world datasets, there is an imbalance in the number of samples belonging
to different classes. Various pattern classification problems such as fault or disease …
to different classes. Various pattern classification problems such as fault or disease …
Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique
Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …
A new sampling method for classifying imbalanced data based on support vector machine ensemble
C Jian, J Gao, Y Ao - Neurocomputing, 2016 - Elsevier
The insufficient information from the minority examples cannot exactly represent the inherent
structure of the dataset, which leads to a low prediction accuracy of the minority through the …
structure of the dataset, which leads to a low prediction accuracy of the minority through the …
KNN weighted reduced universum twin SVM for class imbalance learning
In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …
classification problems as the data samples of a particular class are dominating. Problems …
CDBH: A clustering and density-based hybrid approach for imbalanced data classification
The problem of imbalanced data set classification is prevalent in the studies of machine
learning and data mining. In these kinds of data sets, the number of samples in classes is …
learning and data mining. In these kinds of data sets, the number of samples in classes is …
Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis
A Sharafati, SB Haji Seyed Asadollah… - Hydrological …, 2020 - Taylor & Francis
Ensemble machine learning models have been widely used in hydro-systems modeling as
robust prediction tools that combine multiple decision trees. In this study, three newly …
robust prediction tools that combine multiple decision trees. In this study, three newly …
Protein–protein interaction sites prediction by ensembling SVM and sample-weighted random forests
ZS Wei, K Han, JY Yang, HB Shen, DJ Yu - Neurocomputing, 2016 - Elsevier
Predicting protein–protein interaction (PPI) sites from protein sequences is still a challenge
task in computational biology. There exists a severe class imbalance phenomenon in …
task in computational biology. There exists a severe class imbalance phenomenon in …
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering
Accurately identifying the protein-ligand binding sites or pockets is of significant importance
for both protein function analysis and drug design. Although much progress has been made …
for both protein function analysis and drug design. Although much progress has been made …
A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond
Proteins interact with diverse ligands to perform a large number of biological functions, such
as gene expression and signal transduction. Accurate identification of these protein–ligand …
as gene expression and signal transduction. Accurate identification of these protein–ligand …
ADMET evaluation in drug discovery. 18. Reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches
Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the
urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main …
urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main …