One of the most popular algorithms for classification is the decision tree. However, existing
binary decision tree models do not handle well the minority class over imbalanced data sets …

During past few decades, researchers worked on data preprocessing techniques for the
datasets. Data preprocessing techniques are needed, where the data are prepared for …

The study aims to investigate the factors that are associated with fatal and severe vehicle–
pedestrian crashes in Great Britain by developing four parametric models and five non …

Relational data classification is the problem of predicting a class label of a target entity given
information about features of the entity, of the related entities, or neighbors, and of the links …

Mining high utility itemsets is an interesting research problem in data mining and knowledge
discovery. Most high utility itemset discovery algorithms seek patterns in a single table, but …

Machinery for data analysis often requires a numeric representation of the input. Towards
that, a common practice is to embed components of structured data into a high-dimensional …

Classification is one of the important tasks in data mining in which a model is generated-
based on training dataset and that model is used to predict class label of unknown dataset …

À une époque où les données, souvent interprétées comme une «réalité terrain», sont
produites dans des quantités gargantuesques, un besoin de compréhension et …

The growth of a country strongly depends on the quality of its educational system. All over
the world, the education sectors are experiencing a fundamental evolution of their mode of …

Many databases store data in relational format, with different types of entities and
information about their attributes and links between the entities. Link-based classification …