The Effects of Feature Optimization on High-Dimensional Essay Data

Yi, Bong Jun; Lee, Do Gil; Rim, Hae Chang

doi:10.1155/2015/421642

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Large number of features are often extracted from the ECG signals and, although useful, these features increase the training time and can hamper an optimized training to be performed [70]. Thus, when the feature set has a high dimension, optimization is required in order to reduce the complexity of the models and obtain relevant and accurate classification.…”

Section: Feature Optimization Techniquesmentioning

confidence: 99%

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Cretu

Tindale

Abbod

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Cardiac arrhythmia is a condition caused by an impaired electrical conduction of the heart, resulting in irregular rhythms that can increase the risk of stroke or even lead to sudden cardiac deaths. Luckily, bio-signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) can be utilized to assess the health of the patient. This work presents a comprehensive review on the recent machine learning (ML) and deep learning methods applied for arrhythmia classification using both, ECG and ABP signals, including preliminary steps such as pre-processing, feature extraction and feature optimization.

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Section: Feature Optimization Techniquesmentioning

confidence: 99%

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Cretu

Tindale

Abbod

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

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“…The reason is that the performance of AI methods particularly machine learning algorithms heavily depends on the choice of features or data representation. Having irrelevant features or contextual information in the data makes the model learn based on irrelevant features that consequently decrease the accuracy of the models [132]. Thus the challenge is to effectively select the relevant and important features or extracting new features that are known as feature optimization.…”

Section: Research Issues and Future Directionsmentioning

confidence: 99%

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

et al. 2020

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Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on "mobile data science and intelligent apps" in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-today situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

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“…Several text-mining studies using lexical features have improved their performance using feature selection to remove automatically unnecessary features (Yi et al , 2015). In this study, we utilized ANOVA (Stigler, 1986) among several techniques for feature selection, and selected useful features among the outputs from the feature extractors.…”

Section: Sentence-level Classification System For Business Environmental Analysismentioning

confidence: 99%

Business environmental analysis for textual data using data mining and sentence-level classification

Kim

Rim

Lee

2019

IMDS

Self Cite

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Purpose The purpose of this paper is to propose a methodology to analyze a large amount of unstructured textual data into categories of business environmental analysis frameworks. Design/methodology/approach This paper uses machine learning to classify a vast amount of unstructured textual data by category of business environmental analysis framework. Generally, it is difficult to produce high quality and massive training data for machine-learning-based system in terms of cost. Semi-supervised learning techniques are used to improve the classification performance. Additionally, the lack of feature problem that traditional classification systems have suffered is resolved by applying semantic features by utilizing word embedding, a new technique in text mining. Findings The proposed methodology can be used for various business environmental analyses and the system is fully automated in both the training and classifying phases. Semi-supervised learning can solve the problems with insufficient training data. The proposed semantic features can be helpful for improving traditional classification systems. Research limitations/implications This paper focuses on classifying sentences that contain the information of business environmental analysis in large amount of documents. However, the proposed methodology has a limitation on the advanced analyses which can directly help managers establish strategies, since it does not summarize the environmental variables that are implied in the classified sentences. Using the advanced summarization and recommendation techniques could extract the environmental variables among the sentences, and they can assist managers to establish effective strategies. Originality/value The feature selection technique developed in this paper has not been used in traditional systems for business and industry, so that the whole process can be fully automated. It also demonstrates practicality so that it can be applied to various business environmental analysis frameworks. In addition, the system is more economical than traditional systems because of semi-supervised learning, and can resolve the lack of feature problem that traditional systems suffer. This work is valuable for analyzing environmental factors and establishing strategies for companies.

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The Effects of Feature Optimization on High-Dimensional Essay Data

Cited by 5 publications

References 12 publications

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Business environmental analysis for textual data using data mining and sentence-level classification

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