SVM classification for imbalanced data using conformal kernel transformation

Zhang, Yong; Fu, Panpan; Liu, Wenzhe; Zou, Li

doi:10.1109/ijcnn.2014.6889420

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Zhang et al presented a conformal kernel transformation for SVM to address the class imbalance problem [19]. For this an approximated hyperplane of the SVM was obtained and a kernel function was applied by computing the parameters through the chi-square test.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

et al. 2018

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Abstract:In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance.

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Section: Related Workmentioning

confidence: 99%