Bitcoin price prediction based on people’s opinions Twitter usually requires millions of tweets, using different text mining techniques, and developing a machine learning model to perform the prediction. These attempts lead employment significant amount computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, time. To address this issue, in paper, we consider...

State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level att...

We developed a novel classification of concept attributes and two supervised classifiers using this classification to identify concept attributes from candidate attributes extracted from the Web. Our binary (attribute / non-attribute) classifier achieves an accuracy of 81.82% whereas our 5-way classifier achieves 80.35%.

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, f...

A hybrid algorithm based on attribute reduction of Rough Sets(RS) and classification principles of Support Vector Machine (SVM) to detect new malicious executable codes is present. Firstly, the attribute reduction of RS has been applied as preprocessor so that we can delete redundant attributes and conflicting objects from decision making table but remain efficient information lossless. Then, w...

Most existing rough set-based feature selection algorithms suffer from intensive computation of either discernibility functions or positive regions to find attribute reduct. In this paper, we develop a new computation model based on relative attribute dependency defined as the proportion of the projection of the decision table on a condition attributes subset to the projection of the decision t...

Identity disclosure is one of the most serious privacy concerns in many data mining applications. A wellknown privacy model for protecting identity disclosure is k-anonymity. The main goal of anonymizing classification data is to protect individual privacy while maintaining the utility of the data in building classification models. In this paper, we present an approach based on rough sets for m...

We describe an approach to extract attribute-value pairs from product descriptions in order to augment product databases by representing each product as a set of attribute-value pairs. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. We formulate the extraction task as a classification prob...