Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models

Kim, Soo Y.; Upneja, Arun

doi:10.1016/j.econmod.2013.10.005

Cited by 157 publications

(81 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ocean Wise restaurateurs scored the importance of sustainability significantly higher, both in rank and value, than non-OW restaurateurs. Although rank and value scored low compared to other factors, seafood typically leaves low profit margins (Kim and Upneja 2014) which necessitates high concern for cost. This finding is supported by Poulston and Yiu (2011) who found that profit generation must be prioritized, and cannot always occur concurrently with environmental protection.…”

Section: Discussionmentioning

confidence: 99%

Understanding sustainable seafood consumption behavior: an examination of the Ocean Wise (OW) initiative in British Columbia

Dolmage¹,

Macfarlane²,

Alley³

2016

E&S

View full text Add to dashboard Cite

ABSTRACT. Sustainable seafood labeling programs have been developed as one of several efforts to address the current dire trends in fish stocks. The Ocean Wise (OW) program, started at the Vancouver Aquarium (Canada), works with restaurateurs and suppliers to simplify sustainable purchasing decisions. By aiding restaurateurs with responsible purchasing, OW hopes to shift demand to sustainable seafood products. OW has grown in numbers and spread across Canada quickly; we examine the factors associated with individual and organizational decisions to participate in the program, including personal, business, and program-related factors. These factors were examined in relation to OW membership by Vancouver restaurateurs. Results show that restaurateurs with greater knowledge of seafood issues and restaurants with greater commitment to a range of green initiatives are more likely to participate in the OW program. By focusing efforts on education and incorporating a range of green values into marketing, OW can maximize their limited resources to grow membership.

show abstract

Section: Discussionmentioning

confidence: 99%

Understanding sustainable seafood consumption behavior: an examination of the Ocean Wise (OW) initiative in British Columbia

Dolmage¹,

Macfarlane²,

Alley³

2016

E&S

View full text Add to dashboard Cite

show abstract

“…The difference between the two expresses the information and attributes that generate the gain ratio, and the largest gain ratio among all gain ratios is used to handle the partition task. Each child node is treated again as a new tree, and the process repeats until there is no misclassification in the training data (Kim & Upneja, 2014).…”

Section: Decision Treesmentioning

confidence: 99%

Multi-agent hybrid mechanism for financial risk management

Yan

Liao

Shih

2015

JIEM

View full text Add to dashboard Cite

Abstract:Purpose: The goal of this study was to propose the multi-agent mechanism to forecast the corporate financial distress.Design/methodology/approach: This study utilized numerous methods, namely random subspace method, discriminant analysis and decision tree to construct the multi-agent forecasting model. Findings:The study shows a superior forecasting performance. Originality/value:The use of multi-agent model to predict the corporate financial distress.

show abstract

“…The workflow for processing jobs submitted by the user through MapReduce is as follows: The submitted job is divided into many small pieces; simultaneously the input data is divided into a number of fixed-size modules distributed to each node [10][11]; then, the operation of the node is done in the local. When the operation of the node is complete, the output results need some sort of recombination and resorting.…”

Section: Mapreduce Modelmentioning

confidence: 99%

A Classification Algorithm of CART Decision Tree based on MapReduce Attribute Weights

Zhu¹

2018

IJPE

View full text Add to dashboard Cite

A CART decision tree algorithm based on attribute weight is proposed in this paper because of the present problems of complex classification, poor accuracy, low efficiency, and severe memory consumption of CART decision. What is more, the algorithm is combined with the parallel computing model of MapReduce. Theory of attribute weights is used in the algorithm. A decision tree is built through the sum of weights, which is decided by the degree that the attributes affect a decision. Thus the accuracy of classification through decision tree is improved. Parallel sorting algorithms of CART decision tree for massive data is implemented through the MapReduce programming technology of cloud computing. All the results of theoretical analysis and experimental comparison show that it is very important to mark attributes by weights through MapReduce. Furthermore, the accuracy of the classification of large sample data sets is improved significantly, classification efficiency of decision tree is improved and the trained time is also significantly reduced.

show abstract

Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models

Cited by 157 publications

References 82 publications

Understanding sustainable seafood consumption behavior: an examination of the Ocean Wise (OW) initiative in British Columbia

Understanding sustainable seafood consumption behavior: an examination of the Ocean Wise (OW) initiative in British Columbia

Multi-agent hybrid mechanism for financial risk management

A Classification Algorithm of CART Decision Tree based on MapReduce Attribute Weights

Contact Info

Product

Resources

About