Handling missing attribute values is the greatest challenging process in data analysis. There are so many approaches that can be adopted to handle the missing attributes. In this paper, a comparative analysis is made of an incomplete dataset for future prediction using rough set approach and random tree generation in data mining. The result of simple classification technique (using random tree ...

In test cost-sensitive decision systems, it is difficulty for us to find an optimal attribute set and construct a quality classifier with limited cost. The minimal test cost-sensitive attribute reduction is proposed to address the former problem. However, it is inevitable to remove some good even better attributes in the minimal test cost-sensitive attribute reduction. As a result, the classifi...

Recent works have shown that visual attributes are useful in a number of applications, such as object classification, recognition, and retrieval. However, predicting attributes in images with large variations still remains a challenging problem. Several approaches have been proposed for visual attribute classification; however, most of them assume independence among attributes. In fact, to pred...

One of the most common types social networking interaction is memes. Memes are innately multimodal, so studying and processing them a hot issue currently. This study's analysis DV dataset comprises classifying memes according to their irony, humour, motive, overarching mood. The effectiveness three different creative transformer-based strategies has been carefully examined. Dataset used here cr...

This paper studies the effect of synthetic feature vectors on the classification performance of hyperspectral remote sensing images. As feature vectors, it has been chosen to employ morphological attribute profiles, that have proven themselves in this field. At this early stage of our work, the relatively simple Bootstrapping algorithm has been used for synthetic feature vector generation. Base...