We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines outputs from XGBoost, LightGBM, and CatBoost algorithms create stronger classifiers. A novel step in our pipeline is that prior performing SHEEP first estimates photometric redshifts, are then placed into data set as an additional feature for classification model traini...

Generally, classification problems catalog instances according to their target variable without considering the relation among different labels. However, there are real in which values of class related each other. Because interest this type problem, several solutions have been proposed, such as cost-sensitive classifiers. Ensembles proven be very effective for tasks; however, far we know, no pr...

-Classification and analysis of data streams are the most promising fields of research and development in Data stream mining. Ensemble based classification approach is one the most challenging flavor of developing an efficient classifier due to large number available base classifiers and increase in the computational time required for training and classification. This research emphasizes on dev...

In this project we proposed an ensemble classifier to classify over 20 thousand images sampled from ImageNet, which originally has over 10 million images. One of the challenge of this classification problem is that the images cannot be precisely represented by one type of features, such as SIFT and GIST. Hence, in this project, we use different kinds of features. Another challenge is that diffe...

Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introdu...

accurate quantitative precipitation forecasts (qpfs) have been always a demanding and challenging job in numerical weather prediction (nwp). the outputs of ensemble prediction systems (epss) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (pqpfs). in this research, different configurations of wrf and mm5 meso-scale models form ...

Most range-based recognition systems require the calculation of a full disparity map at adequate resolutions prior to the recognition step. There also exist range-based systems that only require the computation of a sparse disparity map. We introduce a 3D shape classification method in which the disparity calculation is guided by the needs of the classification process. The method uses decision...