Titlebook: Intelligent Information and Database Systems; 14th Asian Conferenc Ngoc Thanh Nguyen,Tien Khoa Tran,Edward Szczerbick Conference proceeding

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A Comparative Study of?Classification and?Clustering Methods from?Text of?Books books on similar topics is very troublesome for both librarians and readers. This is a difficult problem due to the analysis of large sets of real text data, such as the content of books. For this purpose, we propose to create an appropriate model system, the use of which will allow for automatic a

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A Lightweight and?Efficient GA-Based Model-Agnostic Feature Selection Scheme for?Time Series Forecasmany fields. One of the critical issues in dealing with the time series prediction task is how to choose appropriate input features. This paper proposes a novel approach to select a sub-optimal feature combination automatically. Our proposed method is model-agnostic that can be integrated with any p

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The Quality of?Clustering Data Containing Outliersoptions and distance measures) and the . algorithm. We assess the quality of clustering using Davies-Bouldin and Dunn cluster validity indices. Our goal is to compare and analyze outlier detection algorithms depending on the applied clustering algorithm. We also wanted to verify whether the quality

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Aggregated Performance Measures for?Multi-class Classificationassification to use them in hyperparameter estimation for various machine learning methods and similar techniques. The classical approach is to use a binary classification wherein each representative of any incorrect class is considered as a representative of an umbrella class being a union of all i

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