Results 11 to 20 of about 5,420 (166)
An Online Sparse Streaming Feature Selection Algorithm
, 2022 Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the
Chen, Feilong, Wu, Di, Yang, Jie, He, Yi
openaire +2 more sources
Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Applied Sciences, 2021 Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns.
Michael Heigl +3 more
doaj +1 more source
Online algorithms for mining semi-structured data stream [PDF]
2002 IEEE International Conference on Data Mining, 2002. Proceedings., 2003 In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return ...
T. Asai +4 more
openaire +1 more source
Quantum Versus Classical Online Streaming Algorithms with Logarithmic Size of Memory
Lobachevskii Journal of Mathematics, 2023 We consider online algorithms with respect to the competitive ratio. Here, we investigate quantum and classical one-way automata with non-constant size of memory (streaming algorithms) as a model for online algorithms. We construct problems that can be solved by quantum online streaming algorithms better than by classical ones in a case of logarithmic ...
Khadiev, K. +5 more
openaire +3 more sources
Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark
Complexity, 2021 Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter.
Nahla F. Omran +3 more
doaj +1 more source
International Journal of Computational Intelligence Systems, 2020 The goals of feature selection are to remove redundant and irrelevant features from high-dimensional data, extract the “optimal feature subset” of the original feature space to improve the classification accuracy, and reduce the time complexity ...
Hongyi Wang, Dianlong You
doaj +1 more source
Sentiment Analysis on WeTV App Reviews on Google Play Store Using NBC and SVM Algorithms
Sistemasi: Jurnal Sistem Informasi, 2023 Since the Covid-19 outbreak hit Indonesia, all community activities have become very limited. The government's decision regarding PPKM to reduce the level of Covid-19 cases forced the community to reduce the level of activities outside the home including
Petronilia Palinggik Allorerung +1 more
doaj +1 more source
An online classification algorithm for large scale data streams: iGNGSVM [PDF]
Neurocomputing, 2017 Stream Processing has recently become one of the current commercial trends to face huge amounts of data. However, normally these techniques need specific infrastructures and high resources in terms of memory and computing nodes. This paper shows how mini-batch techniques and topology extraction methods can help making gigabytes of data to be manageable
Suárez Cetrulo, Andrés L. +1 more
openaire +2 more sources
IEEE Access, 2019 Early diagnosis significantly improves the survival rate in lung carcinoma patients. This study attempts to construct a predictive network between the computational features and semantic features of pulmonary nodules using online feature selection and ...
Jing Yang +4 more
doaj +1 more source
HTTP Adaptive Streaming Framework with Online Reinforcement Learning
Applied Sciences, 2022 Dynamic adaptive streaming over HTTP (DASH) is an effective method for improving video streaming’s quality of experience (QoE). However, the majority of existing schemes rely on heuristic algorithms, and the learning-based schemes that have recently ...
Jeongho Kang, Kwangsue Chung
doaj +1 more source