Semantic concept detection in imbalanced datasets based on different under-sampling strategies

Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes. However, there is often imbalanced dataset or rare class problems in classification algorithms, which deteriorate the performance of many classifiers. In this paper, we adopt three “under-sampling” strategies to handle this imbalanced dataset issue in a SVM classification framework and evaluate their performances on the TRECVid 2007 dataset and additional positive samples from TRECVid 2010 development set. Experimental results show that our well-designed “under-sampling” methods (method SAK) increase the performance of concept detection about 9.6% overall. In cases of extreme imbalance in the collection the proposed methods worsen the performance than a baseline sampling method (method SI), however in the majority of cases, our proposed methods increase the performance of concept detection substantially. We also conclude that method SAK is a promising solution to address the SVM classification with not extremely imbalanced datasets

Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard)

Hybrid classification approach for imbalanced datasets

The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a classic classifier, the classifier tends to classify all data to majority class by ignoring minority data as noise. Thus, it is very significant to select appropriate training dataset in the prepossessing stage for classification of imbalanced dataset. We propose an combination approach of SMOTE (Synthetic Minority Over-sampling Technique) and instance selection approaches. The numeric results show that the proposed combination approach can help classifiers to achieve better performance

Spiking neurons with short-term synaptic plasticity form superior generative networks

Spiking networks that perform probabilistic inference have been proposed both as models of cortical computation and as candidates for solving problems in machine learning. However, the evidence for spike-based computation being in any way superior to non-spiking alternatives remains scarce. We propose that short-term plasticity can provide spiking networks with distinct computational advantages compared to their classical counterparts. In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively. During training, the energy landscape associated with their dynamics becomes highly diverse, with deep attractor basins separated by high barriers. Classical algorithms solve this problem by employing various tempering techniques, which are both computationally demanding and require global state updates. We demonstrate how similar results can be achieved in spiking networks endowed with local short-term synaptic plasticity. Additionally, we discuss how these networks can even outperform tempering-based approaches when the training data is imbalanced. We thereby show how biologically inspired, local, spike-triggered synaptic dynamics based simply on a limited pool of synaptic resources can allow spiking networks to outperform their non-spiking relatives.Comment: corrected typo in abstrac

Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional neural networks

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