Modeling wine preferences by data mining from physicochemical properties

Cortez, Paulo; Cerdeira, A. L.; Almeida, Fernando; Matos, Telmo; Reis, José Luís

doi:10.1016/j.dss.2009.05.016

Cited by 922 publications

(568 citation statements)

References 24 publications

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“…When comparing DT, NN and SVM, several studies have shown different classification performances. For instance, SVM provided better results in [6] [8], comparable NN and SVM performances were obtained in [5], while DT outperformed NN and SVM in [24]. These differences in performance emphasize the impact of the problem context and provide a strong reason to test several techniques when addressing a problem before choosing one of them [9].…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach to predict the success of bank telemarketing

Moro

Cortez

Rita

2014

Decision Support Systems

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364

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We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine.Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.

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Section: Introductionmentioning

confidence: 99%

A data-driven approach to predict the success of bank telemarketing

Moro

Cortez

Rita

2014

Decision Support Systems

Self Cite

609

364

View full text Add to dashboard Cite

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“…In Fig. 8(a)-(c) we present the quality metrics E St , E Nb , and E Cc , respectively, evaluating the quality of the family of projections generated with ProjInspector during analysis of the wine-quality-red data set [43]. As one can clearly see, the projection that produces a better result changes considerably depending on the metric.…”

Section: Analyzing Projections and Their Quality Measuresmentioning

confidence: 99%

Projection inspector: Assessment and synthesis of multidimensional projections

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“…A large dataset of laboratory analytical test values (11 items) for a group of 1599 red wines (vinho verde samples from Northen regions of Portugal) was taken from literature [3] and used for descriptor fingerprints creation. Descriptor fingerprints were generated on the basis of physicochemical laboratory data routinely used for wine characterization (fixed and volatile acidity; residual sugar, total and free sulfur dioxide, citric acid, chlorides, sulfates, density, pH and alcohol content).…”

Section: Methodsmentioning

confidence: 99%