In this chapter, we will present the data stream mining components. The problem of concept drift in classification algorithms and several existing state﹐f‐the゛rt handling methods are highlighted. Besides, the most used datasets, tools, applications, and evaluation methods will be presented.doi:10.1002/9781119654674.ch11Mashail Alth...
It performs statistical tests like Z-test, Nemenyi test, and Friedman method on datasets. A new stability concept and a change detection algorithm work on unsupervised learning that are explained in Vallim and De Mello (2014). Here, the concept change detection is based on the surrogate data,...
Since then, several studies have focused on the scalability of FCA for efficiently handling large and complex datasets. Scalability is a real issue for FCA, since the number of formal concepts can be exponential in the input context and counting them is #P-complete (Kuznetsov, 2001), however,...
In our experiments, it has been demonstrated that TLP〦nAbLe handles concept drift more effectively than other state﹐f‐the゛rt algorithms on nineteen artificially drifting and ten real﹚orld datasets. Further, statistical tests conducted on various drift patterns which include gradual, abrupt, ...
“Experiments” section describes the experiment settings and the datasets. “Results and discussion” section presents the results of the experiments. Finally, “Conclusion” section concludes the paper. Related work In data stream clustering, there are some crucial requirements to be considered like ...
model or training process. We demonstrate on multiple datasets, model architectures and application domains that CRP-based analyses allow one to (1) gain insights into the representation and composition of concepts in the model as well as quantitatively investigate their role in prediction, (2) iden...
datasets to improve the accuracy and relevance of its outputs, it can also be fine-tuned using known techniques. Exploring different models, including those trained on larger datasets and/or with advanced natural language processing capabilities, could yield even more effective recommendations. ...
concept of data stream size is proposed. Subsequently, we propose the DLVSW-CDTD algorithms to effectively detect different types of CD during the data stream mining process. In the fourth section, extensive experiments are conducted using real and synthetic datasets obtained using the open-source ...
s. When applied to three diverse object detectors and two datasets, our methods reveal that (1) similar semantic concepts are learnedregardless of the CNN architecture, and (2) similar concepts emerge in similarrelativelayer depth, independent of the total number of layers. Finally, our approach...
Four different datasets were used, which are Conclusion In this paper we introduced the novel concept of “similarity-sets”, or SimSets for short. A “set” is a data structure holding a collection of elements that has no duplicates. A SimSet S^ξ is a data structure without any pair ...