A survey of classification accuracy using multifeatures and multi-kernels

Abstract

The bag-of-words (BoW) model is used widely for image classification. In this model, the image-level representations are designed using BoW frameworks from local low-level features, therefore we introduce our local low-level feature, called the denseSBP feature, using for BoW. We will evaluate performance in classification when using this feature. To increase average precision, we combine denseSBP feature with other features using Multiple Kernel Learning (MKL). In this work, we also propose the method called the integrated method, that it based on using multi-features and multi-kernels in SVM classification to derive the best classification accuracy for each category of a dataset. We perform the comparative analysis about classification accuracies of the method using MKL and the integrated method on image benchmark datasets. The experimental results show comparable classification accuracies of proposal methods with the state-of-the-art methods.

IoT Device Identification Based on Network Traffic

“…The trained model will be used for device identi cation. The protocol statistical features use the Bag of Words (BoW) [22] idea, which takes the top-level protocol type of device tra c as the vocabulary, and constructs a word vector for each device according to the vocabulary to represent the device's protocol usage. Flow-level statistical features are derived from bidirectional ow information of devices, which takes the device tra c as input and computes statistics such as ow size, duration and transmission rate.…”