Cost-Sensitive Support Vector Machine for Semi-Supervised Learning

A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers

In machine learning, hyperparameter tuning is strongly useful to improve model performance. In our research, we concentrate our attention on classifying imbalanced data by cost-sensitive support vector machines. We propose a multi-objective approach that optimizes model’s hyper-parameters. The approach is devised for imbalanced data. Three SVM model’s performance measures are optimized. We present the algorithm in a basic version based on genetic algorithms, and as an improved version based on genetic algorithms combined with decision trees. We tested the basic and the improved approach on benchmark datasets either as serial and parallel version. The improved version strongly reduces the computational time needed for finding optimized hyper-parameters. The results empirically show that suitable evaluation measures should be used in assessing the classification performance of classification models with imbalanced data.

Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals

Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when training and testing EEG signal samples are collected from different distributions, for example, due to differences in patient groups or acquisition devices, such methods generally cannot perform well. In this paper, a cross-domain classification model with knowledge utilization maximization called CDC-KUM is presented, which takes advantage of the data global structure provided by the labeled samples in the related domain and unlabeled samples in the current domain. Through mapping the data into kernel space, the pairwise constraint regularization term is combined together the predictive differences of the labeled data in the source domain. Meanwhile, the soft clustering regularization term using quadratic weights and Gini-Simpson diversity is applied to exploit the distribution information of unlabeled data in the target domain. Experimental results show that CDC-KUM model outperformed several traditional non-transfer and transfer classification methods for recognition of epileptic EEG signals.

Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging

One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply oversampling strategies in an attempt to balance the classes and improve classification performance. We evaluated four different classifiers from k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP) and decision trees (DT) with 73 oversampling strategies. In this work, we used imbalanced learning oversampling techniques to improve classification in datasets that are distinctively sparser and clustered. This work reports the best oversampling and classifier combinations and concludes that the usage of oversampling methods always outperforms no oversampling strategies hence improving the classification results.