In this paper we propose an ensemble of local and deep features for object classification. We also compare and contrast effectiveness of feature representation capability of various layers of convolutional neural network. We demonstrate with extensive experiments for object classification that the representation capability of features from deep networks can be complemented with information capt...

If we look a few years back, we will find that ensemble classification model has outbreak many research and publication in the data mining community discussing how to combine models or model prediction with reduction in the error that results. When we ensemble the prediction of more than one classifier, more accurate and robust models are generated. We have convention that bagging, boosting wit...

As more business transactions and information services have been implemented via communication networks, both personal organization assets encounter a higher risk of attacks. To safeguard these, perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has great deal attention within the joint community security data science to improve m...

Numerous prior works have shown how we can use Knowledge Graph Embeddings (KGEs) for ranking unseen facts that are likely to be true. Much less attention has been given on KGEs fact classification, i.e., mark either as true or false. In this paper, tackle problem with a new technique exploits ensemble learning and weak supervision, following the principle multiple classifiers make strong one. O...

Multiple Instance Learning Via Embedded Instance Selection (MILES) has shown good performance in dealing with noisy training samples, but its bag prediction rule may introduce new uncertainty into the remote sensing image classification results. In order to overcome this limitation, two popular ensemble learning strategies, Bagging and AdaBoost are integrated with MILES. Two methods are propose...