Notes and Communications RESOLVING CERTAIN DIFFICULTIES AND IMPROVING THE CLASSIFICATION POWER OF LP DISCRIMINANT ANALYSIS FORMULATIONS

Freed, N.; Glover, Fred

doi:10.1111/j.1540-5915.1986.tb00244.x

Cited by 73 publications

(29 citation statements)

References 5 publications

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“…w c s and w c s may be another alternative for preventing the elimination of optimal solution (Freed and Glover, 1986b). Another normalisation constraint is generated by Erenguc and Koehler (1990) which is formulized as w'= s. However, this constraint may limit the feasible region.…”

Section: Discriminant Analysis By Classical Modelling Methodsmentioning

confidence: 99%

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The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming

Dikmen

Küçükkocaoğlu²

2010

Journal of Forecasting

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The primary goal of this study is to propose an algorithm using mathematical programming to detect earnings management practices 1 . In order to evaluate the ability of this proposed algorithm, the traditional statistical models are used as a benchmark vis-a-vis with their timeseries counterparts. As emerging techniques in the area of mathematical programming yields better results, application of suitable models expected to result in highly performed forecasts. The motivation behind this paper is to develop an algorithm which will give a successful achievement on detecting companies which appeal to financial manipulation. The methodology is based on cutting plane formulation using mathematical programming. A sample of 126 Turkish manufacturing firms described over ten financial ratios and indexes are used for detecting factors associated with false financial statements. The results indicate that the proposed three phase cutting plane algorithm outperforms the traditional statistical techniques which are widely used for false financial statement detections. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of false financial statements and highlight the importance of financial ratios/indexes such as Days' Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Working Capital Accruals to Total Assets (TATA), Days to Inventory Index (DINV).

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Section: Discriminant Analysis By Classical Modelling Methodsmentioning

confidence: 99%

“…Deviation is not considered in this model. MIP-2 (Freed and Glover, 1986b;Glover, 1988;Stam and Joachimstahler, 1988): min I 1 + I 2 X 1 w≤ c+ M I 1 X 2 w≥ c-M I 2…”

Section: Discriminant Analysis By Classical Modelling Methodsmentioning

confidence: 99%

The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming

Dikmen

Küçükkocaoğlu²

2010

Journal of Forecasting

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“…A number of these properties are generalizations of similar properties previously derived, discussed and analyzed for the twogroup case by, among others, Freed and Glover (1986) and Koehler (1989aKoehler ( , 1989bKoehler ( , 1990Koehler ( , 1991.…”

Section: Subject Tomentioning

confidence: 99%

Multigroup Discriminant Analysis Using Linear Programming

Gochet

Stam

Srinivasan

et al. 1997

Operations Research

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In this paper we introduce a nonparametric linear programming formulation for the general multigroup classification problem. Previous research using linear programming formulations has either been limited to the two-group case, or required complicated constraints and many zero-one variables. We develop general properties of our multigroup formulation and illustrate its use with several small example problems and previously published real data sets. A comparative analysis on the real data sets shows that our formulation may offer an interesting robust alternative to parametric statistical formulations for the multigroup discriminant problem.

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“…Second, given a threshold τ , the best data separation can be selected from results determined by different b values. Therefore, the parameter b plays a key role in achieving the desired accuracy and most mathematical programming based classification models use b as an important control parameter [19], [29], [30], [21], [22], [26], [28], [22], [39].…”

Section: Introductionmentioning

confidence: 99%

Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level Programming for Intelligent Credit Scoring

Zhang

Shi³

et al. 2010

IEEE Trans. Knowl. Data Eng.

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Abstract-Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various datasets validated the effectiveness and efficiency of the proposed methods.

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Notes and Communications RESOLVING CERTAIN DIFFICULTIES AND IMPROVING THE CLASSIFICATION POWER OF LP DISCRIMINANT ANALYSIS FORMULATIONS

Cited by 73 publications

References 5 publications

The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming

The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming

Multigroup Discriminant Analysis Using Linear Programming

Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level Programming for Intelligent Credit Scoring

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