C
Chongsheng Zhang
Researcher at Henan University
Publications - 20
Citations - 444
Chongsheng Zhang is an academic researcher from Henan University. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 5, co-authored 12 publications receiving 274 citations.
Papers
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An up-to-date comparison of state-of-the-art classification algorithms
TL;DR: It is found that Stochastic Gradient Boosting Trees (GBDT) matches or exceeds the prediction performance of Support Vector Machines and Random Forests, while being the fastest algorithm in terms of prediction efficiency.
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On Incremental Learning for Gradient Boosting Decision Trees
TL;DR: iGBDT is a novel algorithm that incrementally updates the classification model built upon gradient boosting decision tree (GBDT), namely iGBDT, to incrementally learn a new model but without running GBDT from scratch, when new data is dynamically arriving in batch.
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Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem
TL;DR: A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal).
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An empirical evaluation of high utility itemset mining algorithms
TL;DR: d2HUP is more efficient than EFIM under low minimum utility values and with large sparse datasets, in terms of running time; although EFIM is the fastest in dense real datasets, it is among the slowest algorithms in sparse datasets.
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An Empirical Comparison of Latest Data Clustering Algorithms with State-of-the-Art
TL;DR: It is found that the comparison of different clustering algorithms is closely related to the clustering evaluation metrics adopted, and when using the Silhouette clustering validation metric, the overall performance of K-Means is as good as AP and DP.