33,232 research outputs found
Prediction of protein-protein interactions using one-class classification methods and integrating diverse data
This research addresses the problem of prediction of protein-protein interactions (PPI)
when integrating diverse kinds of biological information. This task has been commonly
viewed as a binary classification problem (whether any two proteins do or do not interact)
and several different machine learning techniques have been employed to solve this
task. However the nature of the data creates two major problems which can affect results.
These are firstly imbalanced class problems due to the number of positive examples (pairs
of proteins which really interact) being much smaller than the number of negative ones.
Secondly the selection of negative examples can be based on some unreliable assumptions
which could introduce some bias in the classification results.
Here we propose the use of one-class classification (OCC) methods to deal with the task of
prediction of PPI. OCC methods utilise examples of just one class to generate a predictive
model which consequently is independent of the kind of negative examples selected; additionally
these approaches are known to cope with imbalanced class problems. We have
designed and carried out a performance evaluation study of several OCC methods for this
task, and have found that the Parzen density estimation approach outperforms the rest. We
also undertook a comparative performance evaluation between the Parzen OCC method
and several conventional learning techniques, considering different scenarios, for example
varying the number of negative examples used for training purposes. We found that the
Parzen OCC method in general performs competitively with traditional approaches and in
many situations outperforms them. Finally we evaluated the ability of the Parzen OCC
approach to predict new potential PPI targets, and validated these results by searching for
biological evidence in the literature
An empirical evaluation of imbalanced data strategies from a practitioner's point of view
This research tested the following well known strategies to deal with binary
imbalanced data on 82 different real life data sets (sampled to imbalance rates
of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline
(just the base classifier). As base classifiers we used SVM with RBF kernel,
random forests, and gradient boosting machines and we measured the quality of
the resulting classifier using 6 different metrics (Area under the curve,
Accuracy, F-measure, G-mean, Matthew's correlation coefficient and Balanced
accuracy). The best strategy strongly depends on the metric used to measure the
quality of the classifier. For AUC and accuracy class weight and the baseline
perform better; for F-measure and MCC, SMOTE performs better; and for G-mean
and balanced accuracy, underbagging
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Learning from class-imbalanced data continues to be a common and challenging
problem in supervised learning as standard classification algorithms are
designed to handle balanced class distributions. While different strategies
exist to tackle this problem, methods which generate artificial data to achieve
a balanced class distribution are more versatile than modifications to the
classification algorithm. Such techniques, called oversamplers, modify the
training data, allowing any classifier to be used with class-imbalanced
datasets. Many algorithms have been proposed for this task, but most are
complex and tend to generate unnecessary noise. This work presents a simple and
effective oversampling method based on k-means clustering and SMOTE
oversampling, which avoids the generation of noise and effectively overcomes
imbalances between and within classes. Empirical results of extensive
experiments with 71 datasets show that training data oversampled with the
proposed method improves classification results. Moreover, k-means SMOTE
consistently outperforms other popular oversampling methods. An implementation
is made available in the python programming language.Comment: 19 pages, 8 figure
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