Markov Boundary Learning With Streaming Data for Supervised Classification

Fuzzy Data Envelopment Analysis for Classification of Streaming Data

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

Fuzzy Data Envelopment Analysis for Classification of Streaming Data

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

Supervised Learning for Classification

Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method. It make use of the class membership of each data to be trained in the case of multiple classes, to improve the quality of classificat...

Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...

Data Mining: Classification/Supervised Learning Potpourri Contents