Data Classification Using Machine Learning Approach

Pandey, Shekhar; Supriya, M.; Shrivastava, Abhilash

doi:10.1007/978-3-319-68385-0_10

Cited by 17 publications

(7 citation statements)

References 6 publications

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“…Effective curation of content in this way can ultimately lead to improvements in the overall business model, such as in developing better ranking systems for travel destinations (Ghose et al, 2012). Furthermore, as our model's performance is acceptable when applied to future data in comparison to training data, news articles and other types of content on new and emergent topics could be classified automatically using the neural network approach we have demonstrated, similar to the way in which machine learning models have been applied for the classification of new products entering a market into existing categories of product types (Pandey et al, 2018).…”

Section: Discussionmentioning

confidence: 87%

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Salminen

Yoganathan

Corporan³

et al. 2019

Journal of Business Research

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As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification-Random Forest, K-Nearest Neighbor, and Neural Network-to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.

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Section: Discussionmentioning

confidence: 87%

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Salminen

Yoganathan

Corporan³

et al. 2019

Journal of Business Research

View full text Add to dashboard Cite

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“…expected results, such as readmission, the beginning of specific disease and the length of stay (los) are the most absolute predictive outcomes (gampala, et al 2020 and natarajan et al 2020). Data preprocessing, alteration have been utilized (Peneti, et al 2021) to generate knowledge regarding the interplay between several measures and patient survival. In the variety of resolution policies, 02 split information removal methods were utilized to extract knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…As customers become constantly aware of the adaptation of their insurance buys to their unique demands, the top health care provider has started evaluating how machine learning (Ml) can improve business and buyer loyalty. The classification of data with machine learning algorithms will effectively collect billions of their current items (Pandey et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Implementation of Machine Learning Techniques for Effective Predictive Analysis in Health Care Management

Prasad¹

2021

Biosc Biotech Res Comm

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The health division has seen a huge transformation subsequent the introduction of latest computer technology & this has led to added medicinal information producing various sectors of research. In recent years, there has been surges in interest in study on assessment sustain appliances in healthcare, for example individuals relating to analysis, forecast, behavior forecast, and so on. This progress is due to increased data availability, breakthroughs in artificial intelligence and machine learning research, and contact to computational assets. Data Mining and Predictive Analysis are being used by a number of healthcare organizations. Predictive analysis utilizes assortment of statistical procedures as well as representation, machine learning & data mining to approximate the future by breaking into past and current realism. Appropriate to the information ambitious environment of machine learning algorithms, artificial intelligence trends has achieved its full prospective when back up by large information positions. Our study examines the implications of current breakthroughs in information analytics and how these will be used to the healthcare industry, with a focus on analytical & prophecy applications.

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“…In prediction, generally machine learning algorithms are used and respective metrics are used in evaluation. Such procedure is done works such as [18], [19].…”

Section: Literature Surveymentioning

confidence: 99%

Helpfulness Prediction of Product Assessments using Machine Learning

2019

ijrte

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Paper Customers express their opinion on products through reviews. Since there will be a lot of reviews that will be posted, only those reviews which are helpful should be made accessible to the customer. Hence, helpfulness of review needs to be predicted. This work categorizes the features into reviewer, review text and review metadata. Machine Learning algorithms Linear Regression and Random Forests are used for prediction of helpfulness using these features. It is observed that rating of a review has the highest influence on predicting helpfulness followed by user average rating deviation, difficult words and positive words. This work defines the features such as stem sim length and lem sim length which are derived from the product description which have performed reasonably well. Using all the features with Random Forests algorithm for prediction gave the best performance in automatically predicting helpfulness.

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Data Classification Using Machine Learning Approach

Cited by 17 publications

References 6 publications

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Implementation of Machine Learning Techniques for Effective Predictive Analysis in Health Care Management

Helpfulness Prediction of Product Assessments using Machine Learning

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