Intelligent Data Analysis

Artificial or simulated data are particularly relevant in tests and benchmarks for machine learning methods, in teaching for exercises and for setting up analysis workflows. They are relevant when real data may not be used for reasons of data ...

Human activity recognition is a key technology in intelligent video surveillance and an important research direction in the field of computer vision. However, the complexity of human interaction features and the differences in motion ...

Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is ...

Spectral clustering has been an effective clustering method, in last decades, because it can get an optimal solution without any assumptions on data’s structure. The basic key in spectral clustering is its similarity matrix. Despite many empirical ...

This paper proposed a new semi-supervised algorithm combined with Mutual-cross Imperial Competition Algorithm (MCICA) optimizing Support Vector Machine (SVM) for motion imagination EEG classification, which not only reduces the tedious and time-...

Multiclass data classification, where the goal is to segment data into classes, is an important task in machine learning. However, the task is challenging due to reasons including the scarcity of labeled training data; in fact, most machine ...

Proper data items distribution may seriously improve the performance of data processing in distributed environment. However, typical datastorage systems as well as distributed computational frameworks do not pay special attention to that aspect. ...

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and ...

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when ...

In the microblog network, users’ forwarding behavior is widespread and the propagation range is difficult to predict quantitatively. To solve this problem, machine learning algorithms are used to quantitatively predict propagation breadth and ...

Instance segmentation has a wide range of applications, including video surveillance, autonomous driving, and behavior analysis. Nevertheless, as a type of pixel-level segmentation, its prediction performance in practice is substantially affected ...

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required ...

Emotion recognition in conversations is crucial as there is an urgent need to improve the overall experience of human-computer interactions. A promising improvement in this field is to develop a model that can effectively extract adequate contexts ...