The increasing volume of generated crime information readily available on the web makes the process of retrieving and analyzing and use of the valuable information in such texts manually a very difficult task. This work is focus on designing models for extracting crime-specific information from the Web. Thus, this paper proposes an ensemble framework for crime named entity recognition task. The...

Defective modules in the software pose considerable risk by decreasing customer satisfaction and by increasing the development and maintenance costs. Therefore, in software development life cycle, it is essential to predict defective modules in the early stage so as to improve software developers' ability to identify the defect-prone modules and focus quality assurance activities. Many res...

Collaborative Representation Classification (CRC) for face recognition attracts a lot attention recently due to its good recognition performance and fast speed. Compared to Sparse Representation Classification (SRC), CRC achieves a comparable recognition performance with 10-1000 times faster speed. In this paper, we propose to ensemble several CRC models to promote the recognition rate, where e...

The relationship between ensemble classifier performance and the diversity of the predictions made by ensemble base classifiers is explored in the context of heterogeneous ensemble classifiers. Specifically, numerical studies indicate that heterogeneous ensembles can be generated from base classifiers of homogeneous ensemble classifiers that are both significantly more accurate and diverse than...

Deep learning (DL) classification has become a major research topic in the areas of cancer prediction, image cell classification, and medicine. Furthermore, DL is core other subfields. Owing to various forms ensemble models, models have achieved state-of-the-art performances fields such as However, existing cannot solve problem generalization perfectly proposed solutions only for tasks with spe...

This paper describes the submission of team IIP in SemEval-2016 Task 4 Subtask A. The presented system is a novel weighted sum ensemble approach for sentiment analysis of short informal texts. The ensemble combines member classifiers that output classification confidence metrics. For the ensemble classification decision the members are combined by weights. In the presented approach the weights ...

Classification of data streams has become an important area of data mining, as the number of applications facing these challenges increases. In this paper, we propose a new ensemble learning method for data stream classification in presence of concept drift. Our method is capable of detecting changes and adapting to new concepts which appears in the stream. Data stream classification; concept d...