Fundamenta Informaticae - Volume 182, issue 2

Authors: Ślęzak, Dominik | Hong, Tzung-Pei | Wang, Leon S.L.

Article Type: Other

DOI: 10.3233/FI-2021-2067

Citation: Fundamenta Informaticae, vol. 182, no. 2, pp. i-ii, 2021

Authors: Le, Linh | Xie, Ying | Raghavan, Vijay V.

Article Type: Research Article

Abstract: The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we present two loss functions, namely KNN Loss and Fuzzy KNN Loss, to quantify the …quality of neighborhoods formed by KNN with respect to supervised learning, such that minimizing the loss function on the training data leads to maximizing KNN decision accuracy on the training data. We further present a deep learning strategy that is able to learn, by minimizing KNN loss, pairwise similarities of data that implicitly maps data to a feature space where the quality of KNN neighborhoods is optimized. Experimental results show that this deep learning strategy (denoted as Deep KNN) outperforms state-of-the-art supervised learning methods on multiple benchmark data sets. Show more

Keywords: KNN Loss, Fuzzy deep learning, deep KNN, Supervised Learning

DOI: 10.3233/FI-2021-2068

Citation: Fundamenta Informaticae, vol. 182, no. 2, pp. 95-110, 2021

Authors: Chelly Dagdia, Zaineb | Zarges, Christine

Article Type: Research Article

Abstract: In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based …on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data feature selection. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH-dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH-dRST is scalable when dealing with large amounts of data. We also demonstrate that LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost. Show more

Keywords: Granular Computing, Rough Set Theory, Big Data, Feature Selection, Locality Sensitive Hashing, Distributed Processing

DOI: 10.3233/FI-2021-2069

Citation: Fundamenta Informaticae, vol. 182, no. 2, pp. 111-179, 2021

Authors: Tsumoto, Shusaku | Hirano, Shoji | Kimura, Tomohiro | Iwata, Haruko

Article Type: Research Article

Abstract: Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, …it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods. Show more

Keywords: Clinical pathway mining, Hierarchical clustering, EM clustering, Hospital information syst Big data analytics

DOI: 10.3233/FI-2021-2070

Citation: Fundamenta Informaticae, vol. 182, no. 2, pp. 181-218, 2021