Evaluating the effect of gap size in a single function mathematical programming model for the three-group classification problem

Pavur, Robert; Loucopoulos, Constantine

doi:10.1057/palgrave.jors.2601116

Cited by 6 publications

(2 citation statements)

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“…Simulation results show that GMFC and MIP3G can outperform the parametric procedures in some nonnormal data sets and that the inclusion of second-order terms can improve the performance D Disease Diagnosis: Optimization-Based Methods of MIP3G in some data sets. Pavur and Loucopoulos [95] investigated the effect of the gap size in the MIP3G model for the three-group classification problem. A simulation study illustrates that for fairly separable data, or data with small sample sizes, a non-zero-gap model can improve the performance.…”

Section: Two-group Classificationmentioning

confidence: 99%

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Discretely Distributed Stochastic Programs: Descent Directions and Efficient Points

Marti¹

2008

Encyclopedia of Optimization

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Article Outline SynonymsIntroduction Definitions Example Applications Customer Relationship Management (CRM) Credit Scoring Bankruptcy Prediction Fraud Detection Ethical Issues in Data Mining Conclusions References Synonyms Data mining; Large-scale data analysis; Pattern recognition D Data MiningBusiness understanding: Business understanding includes determining business objectives, assessing the current situation, establishing data mining goals, and developing a project plan. Data understanding: Once business objectives and the project plan are established, data understanding considers data requirements. This step can include initial data collection, data description, data exploration, and the verification of data quality. Data exploration such as viewing summary statistics (which includes the visual display of categorical variables) can occur at the end of this phase. Models such as cluster analysis can also be applied during this phase, with the intent of identifying patterns in the data. Data preparation: Once the data resources available are identified, they need to be selected, cleaned, built into the form desired, and formatted. Data cleaning and data transformation in preparation for data modeling needs to occur in this phase. Data exploration at a greater depth can be applied during this phase, and additional models utilized, again providing the opportunity to see patterns based on business understanding. Modeling: Data mining software tools such as visualization (plotting data and establishing relationships) and cluster analysis (to identify which variables go well together) are useful for initial analysis. Tools such as generalized rule induction can develop initial association rules. Once greater data understanding is gained (often through pattern recognition triggered by viewing model output), more detailed models appropriate to the data type can be applied. The division of data into training and test sets is also needed for modeling (sometimes even more sets are needed for model refinement). Evaluation: Model results should be evaluated in the context of the business objectives established in the first phase (business understanding). This will lead to the identification of other needs (often through pattern recognition), frequently reverting to prior phases of CRISP-DM. Gaining business understanding is an iterative procedure in data mining, where the results of various visualization, statistical, and artificial intelligence tools show the user new relationships that provide a deeper understanding of organizational operations.Deployment: Data mining can be used both to verify previously held hypotheses, and for knowledge discovery (identification of unexpected and useful relationships). Through the knowledge discovered in the earlier phases of the CRISP-DM process, sound models can be obtained that may then be applied to business operations for many purposes, including prediction or identification of key situations. These models need to be monitored for changes in operating conditions, because what ...

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Section: Two-group Classificationmentioning

confidence: 99%

“…Multigroup classification [41,93] Three-group classification [71,72,95] Classification with reserved-judgment region using mixed integer programming [17,39,40,59,60]…”

Section: Two-group Classificationmentioning

confidence: 99%