The Data Dimensionality Reduction and Features Weighting in the Classification Process Using Forest Optimization Algorithm

Kostrzewa, Daniel; Brzeski, Robert

doi:10.1007/978-3-030-14132-5_8

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…This step determined the importance of a given feature, and then we used this information within the classification process. The word information is important here, as it is the degree of importance of a given feature (i.e., informativeness) on the quality of the score prediction (Kostrzewa & Brzeski, 2019; Pavlenko, 2003). In other words, not all features are important for score prediction, and higher accuracy can be obtained by selecting an informative feature subset.…”

Section: Methodsmentioning

confidence: 99%

Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing

Latifi

Gierl

2020

Language Testing

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An automated essay scoring (AES) program is a software system that uses techniques from corpus and computational linguistics and machine learning to grade essays. In this study, we aimed to describe and evaluate particular language features of Coh-Metrix for a novel AES program that would score junior and senior high school students’ essays from their large-scale assessments. Specifically, we studied nine categories of Coh-Metrix features for developing prompt-specific AES scoring models for our sample. We developed the models by capitalizing on the nine features’ informativeness as a function of dimensionality reduction. We used a three-staged scoring framework. The machine scores were validated against a “gold standard” of ratings, that is, those assigned by two human raters. The nine language features reliably captured the construct of the students’ writing quality. We performed a secondary analysis to see how the scoring models performed in relation to other, already established AES systems, and there was no systematic pattern of scoring discrepancy. However, for essays with widely divergent human ratings, the scoring models were disadvantaged owing to the inherent unreliability of the human scores.

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Section: Methodsmentioning

confidence: 99%

Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing

Latifi

Gierl

2020

Language Testing

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“…Foundation https://www.r-project.org/) were used to enable extraction of meaningful insights from the collected data. It referred to the technique of data preparation, i.e., cleaning, feature selection, and organising raw data to make them suitable to build and train more accurate predictive models [8,9].…”

Section: Statisticsmentioning

confidence: 99%

POL-RISK: an algorithm for 10-year fracture risk prediction in the postmenopausal women from the RAC-OST-POL Study

Pluskiewicz¹,

Adamczyk²,

Werner³

et al. 2023

Polish Archives of Internal Medicine

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The study resulted in the development of an entirely new, original diagnostic tool for predicting osteoporotic fractures. Due to a 10-year prospective observation model in an epidemiological, population-representative study cohort, medical practitioners obtained the possibility of predicting fractures, which may be useful in everyday medical practice. Such a modification of the diagnostic approach should lead to the implementation of therapy adequately to the calculated risk of fractures. The widespread use of a new, easily available diagnostic tool may result in the reduction of new fractures and diminish the costs associated with osteoporosis for the healthcare system.

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The Data Dimensionality Reduction and Features Weighting in the Classification Process Using Forest Optimization Algorithm

Cited by 2 publications

References 19 publications

Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing

Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing

POL-RISK: an algorithm for 10-year fracture risk prediction in the postmenopausal women from the RAC-OST-POL Study

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