Category «Researches»

Solar irradiance forecasting by using wavelet based denoising

Predicting of global solar irradiance is very important in applications using solar energy resources. Due to the fact that in many applications, the data collected includes noise from different sources. The noise probably would have a great influence in the process of building regression models for irradiance forecasting. Denoising based on wavelet transformation as a preprocessing …

A grid density based framework for classifying streaming data in the presence of concept drift

Tegjyot Singh Sethi, Mehmed Kantardzic and  Hanquing Hu Mining data streams is the process of extracting information from non-stopping, rapidly flowing data records to provide knowledge that is reliable and timely. Streaming data algorithms need to be one pass and operate under strict limitations of memory and response time. In addition, the classification of streaming data requires learning in …

On the Reliable Detection of Concept Drift from Streaming Unlabeled Data (Tegjyot Singh Sethi and Mehmed Kantardzic)

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and …

An improved input parameters-insensitive trajectory clustering algorithm.

Abstract: The existing trajectory clustering (TRACLUS) is sensitive to the input parameters ε and MinLns. The parameter valueis changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster (SPSTC) algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line …

Sliding Reservoir Approach for Delayed Labeling in Streaming Data Classification

Hanqing Hu, Mehmed Kantardzic Download Paper Abstract When concept drift occurs within streaming data, a streaming data classification framework needs to update the learning model to maintain its performance. Labeled samples required for training a new model are often unavailable immediately in real world applications. This delay of labels might negatively impact the performance of traditional …