Cumulative Gains Model Quality Metric

Brandenburger, Thomas; Furth, Alfred

doi:10.1155/2009/868215

Cited by 6 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The description of the obtained network is presented in table 2. Brandenburger and Furth (2009). Gains chart is a graphical presentation of the usefulness of the model for predicting the value of dependent categorical variable assuming two values.…”

Section: Resultsmentioning

confidence: 99%

Neural Modelling for the Analysis of Changes in Selected Features of Plant Products

Trajer¹,

Golisz²,

Ratajski³

2017

Farm Machinery and Processes Management in Sustainable Agriculture, IX International Scientific Symposium

View full text Add to dashboard Cite

The work investigates possibilities of plant products quality assessment by means of neural networks. A quick method of plant products assessment was proposed based on the correlations occurring between selected features of plant products and neural modelling. This approach facilitates sustainable agricultural production, which often requires making decisions based on approximate but quick assessment of the quality of produced or processed products. The method of quality assessment is presented using changes in the features of pumpkin being dried as an example. Changes in selected features of chemical composition and colour were analysed, including correlations between them. Initial analysis involved cluster analysis, which allowed for grouping data into cases characterized by similar quality. Based on the analysis, a neural model was developed, which, based on easily obtainable features, allowed for classification of products according to their quality features. This approach was positively verified based on the results of chemical composition and quality assessment performed using statistical analysis of data.

show abstract

Section: Resultsmentioning

confidence: 99%

Neural Modelling for the Analysis of Changes in Selected Features of Plant Products

Trajer¹,

Golisz²,

Ratajski³

2017

Farm Machinery and Processes Management in Sustainable Agriculture, IX International Scientific Symposium

View full text Add to dashboard Cite

show abstract

“…In data mining community, several statistical methods are used to assess the performance of a given model such as the Kolmogorov-Smirnov (K-S) statistics [192,193], decile analysis [171,194,195], lift charts [171,172], and cumulative gains chart [173,190,193]. These methods aim at identifying the model that generates the highest gain with the least cost [171].…”

Section: Model Performance Evalution Methodsmentioning

confidence: 99%

“…These methods aim at identifying the model that generates the highest gain with the least cost [171]. In general, they follow two approaches: evaluating more than one competing model at the same time, or assessing one model only by including cost evaluation metrics in the data mining process [193,196].…”

Section: Model Performance Evalution Methodsmentioning

confidence: 99%

A situation refinement model for complex event processing

Alakari

Gebali

2020

Knowledge-Based Systems

View full text Add to dashboard Cite

“…Visual indicators mainly include ROC curve [43] and AUC [44], precision-recall curve (also known as PR curve) [45], gain curve [46], K-S curve and K-S statistical value [47], and lift curve [48] and lift value.…”

Section: Visual Indicatorsmentioning

confidence: 99%

Application of BigML in the Classification Evaluation of Top Coal Caving

Wang

Tai

Zhang

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Longwall top coal caving mining is one of the main methods of mining thick coal seams in China. Therefore, carrying out the classification evaluation of top coal caving is of great significance to ensure mining success and reduce the risk of mining technology. In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. Furthermore, using the data from the CNKI database as sample data, a classification evaluation model of top coal caving is established on BigML. After training, testing, and optimization, the model is used to evaluate the top coal caving in No. 3 coal seam of Gucheng Coal Mine, and the evaluation result is grade 1, which is consistent with the engineering practice. The final research results show that the application of BigML in the classification evaluation of top coal caving is successful; the evaluation of top coal caving through BigML is reliable; BigML provides another scientific reliability way for the classification evaluation of top coal caving.

show abstract

Cumulative Gains Model Quality Metric

Cited by 6 publications

References 1 publication

Neural Modelling for the Analysis of Changes in Selected Features of Plant Products

Neural Modelling for the Analysis of Changes in Selected Features of Plant Products

A situation refinement model for complex event processing

Application of BigML in the Classification Evaluation of Top Coal Caving

Contact Info

Product

Resources

About