Relevant bibliographies by topics / Linear Scoring

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear Scoring'

Author: Grafiati
Published: 11 December 2022
Last updated: 26 January 2023

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Journal articles on the topic "Linear Scoring"

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Parry, Matthew. "Linear scoring rules for probabilistic binary classification." Electronic Journal of Statistics 10, no. 1 (2016): 1596–607. http://dx.doi.org/10.1214/16-ejs1150.

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Shultz, Kenneth S. "Increasing Alpha Reliabilities of Multiple-Choice Tests with Linear Polychotomous Scoring." Psychological Reports 77, no. 3 (1995): 760–62. http://dx.doi.org/10.2466/pr0.1995.77.3.760.

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Little research has been conducted on the use of linear polychotomous scoring of multiple-choice test items. Therefore, several tests were analyzed using both dichotomous and polychotomous scoring of test items to assess how the alpha reliabilities of the tests change based on the type of scoring used. In each case, the alpha reliabilities of the tests increased, with the same or fewer number of items in each test, when polychotomous (vs dichotomous) scoring of multiple-choice test items was used.
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Trajdos, Pawel, and Robert Burduk. "Probability-driven scoring functions in combining linear classifiers." JUCS - Journal of Universal Computer Science 28, no. 3 (2022): 269–91. http://dx.doi.org/10.3897/jucs.80747.

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Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting. Consequently, linear classifiers are often used as base classifiers of multiple ensemble classification systems. This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space. Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The comparison is done using multiple quality criteria. The statistical analysis of the obtained results is also performed. The experimental study shows that, under certain conditions, some improvement may be obtained.
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Shim, Ju-Hyun, and Young-K. Lee. "Generalized Partially Linear Additive Models for Credit Scoring." Korean Journal of Applied Statistics 24, no. 4 (2011): 587–95. http://dx.doi.org/10.5351/kjas.2011.24.4.587.

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Lev, Omer, and Jeffrey S. Rosenschein. "Convergence of Iterative Scoring Rules." Journal of Artificial Intelligence Research 57 (December 11, 2016): 573–91. http://dx.doi.org/10.1613/jair.5187.

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In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach plausible outcomes, which are stable states, i.e., a situation where no agent would wish to change its vote. One possibility is an iterative process in which, after everyone initially votes, participants may change their votes, one voter at a time. This technique, explored in previous work, converges to a Nash equilibrium when Plurality voting is used, along with a tie-breaking rule that chooses a winner according to a linear order of preferences over candidates. In this paper, we both consider limitations of the iterative voting method, as well as expanding upon it. We demonstrate the significance of tie-breaking rules, showing that no iterative scoring rule converges for all tie-breaking. However, using a restricted tie-breaking rule (such as the linear order rule used in previous work) does not by itself ensure convergence. We prove that in addition to plurality, the veto voting rule converges as well using a linear order tie-breaking rule. However, we show that these two voting rules are the only scoring rules that converge, regardless of tie-breaking mechanism.
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Hu, Tianming, Sam Yuan Sung, Jing Sun, Xiao-Wei Ai, and Peter A. Ng. "A linear transform scheme for building weighted scoring rules1." Intelligent Data Analysis 16, no. 3 (2012): 383–407. http://dx.doi.org/10.3233/ida-2012-0530.

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Hu, C., X. Li, and J. Liang. "Developing optimal non-linear scoring function for protein design." Bioinformatics 20, no. 17 (2004): 3080–98. http://dx.doi.org/10.1093/bioinformatics/bth369.

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Ustun, Berk, and Cynthia Rudin. "Supersparse linear integer models for optimized medical scoring systems." Machine Learning 102, no. 3 (2015): 349–91. http://dx.doi.org/10.1007/s10994-015-5528-6.

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Lee, J. W., S. H. Um, J. B. Lee, J. Mun, and H. Cho. "Scoring and Staging Systems Using Cox Linear Regression Modeling and Recursive Partitioning." Methods of Information in Medicine 45, no. 01 (2006): 37–43. http://dx.doi.org/10.1055/s-0038-1634034.

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Summary Objectives: Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians’ experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. Methods: We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. Results: The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. Conclusions: This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.
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Koh, Wei Yang Calvin, Hong Qi Tan, Khong Wei Ang, Sung Yong Park, Wen Siang Lew, and James Cheow Lei Lee. "Standardizing Monte Carlo simulation parameters for a reproducible dose-averaged linear energy transfer." British Journal of Radiology 93, no. 1112 (2020): 20200122. http://dx.doi.org/10.1259/bjr.20200122.

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Objective: Dose-averaged linear energy transfer (LETD) is one of the factors which determines relative biological effectiveness (RBE) for treatment planning in proton therapy. It is usually determined from Monte Carlo (MC) simulation. However, no standard simulation protocols were established for sampling of LETD. Simulation parameters like maximum step length and range cut will affect secondary electrons production and have an impact on the accuracy of dose distribution and LETD. We aim to show how different combinations of step length and range cut in GEANT4 will affect the result in sampling of LETD using different MC scoring methods. Methods: In this work, different step length and range cut value in a clinically relevant voxel geometry were used for comparison. Different LETD scoring methods were established and the concept of covariance between energy deposition per step and step length is used to explain the differences between them. Results: We recommend a maximum step length of 0.05 mm and a range cut of 0.01 mm in MC simulation as this yields the most consistent LETD value across different scoring methods. Different LETD scoring methods are also compared and variation up to 200% can be observed at the plateau of 80 MeV proton beam. Scoring Method one has one of the lowest percentage differences compared across all simulation parameters. Conclusion: We have determined a set of maximum step length and range cut parameters to be used for LETD scoring in a 1 mm voxelized geometry. LETD scoring method should also be clearly defined and standardized to facilitate cross-institutional studies. Advances in knowledge: Establishing a standard simulation protocol for sampling LETD would reduce the discrepancy when comparing data across different centres, and this can improve the calculation for RBE.
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Dissertations / Theses on the topic "Linear Scoring"

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Millett, Ronald. "Holistic Scoring of ESL Essays Using Linguistic Maturity Attributes." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/762.

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Automated scoring of essays has been a research topic for some time in computational linguistics studies. Only recently have the particular challenges of automatic holistic scoring of ESL essays with their high grammatical, spelling and other error rates been a topic of research. This thesis evaluates the effectiveness of using statistical measures of linguistic maturity to predict holistic scores for ESL essays using several techniques. Selected linguistic attributes include parts of speech, part-of-speech patterns, vocabulary density, and sentence and essay lengths. Using customized algorithms based on multivariable regression analysis as well as memory-based machine learning, holistic scores were predicted on test essays within ±1.0 of the scoring level of human judges' scores successfully an average of 90% of the time. This level of prediction is an improvement over a 66% prediction level attained in a previous study using customized algorithms.
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Voorduin, Raquel. "A non-parametric procedure to estimate a linear discriminant function with an application to credit scoring." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/3710/.

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The present work studies the application of two group discriminant analysis in the field of credit scoring. The view here given provides a completely different approach to how this problem is usually targeted. Credit scoring is widely used among financial institutions and is performed in a number of ways, depending on a wide range of factors, which include available information, support data bases, and informatic resources. Since each financial institution has its own methods of measuring risk, the ways in which an applicant is evaluated for the concession of credit for a particular product are at least as many as credit concessioners. However, there exist certain standard procedures for different products. For example, in the credit card business, when databases containing applicant information are available, usually credit score cards are constructed. These score cards provide an aid to qualify the applicant and decide if he or she represents a high risk for the institution or, on the contrary, a good investment. Score cards are generally used in conjunction with other criteria, such as the institution's own policies. In building score cards, generally parametric regression based procedures are used, where the assumption of an underlying model generating the data has to be made. Another aspect is that, in general, score cards are built taking into consideration only the probability that a particular applicant will not default. In this thesis, the objective will be to present a method of calculating a risk score that, does not depend on the actual process generating the data and that takes into account the costs and profits related to accepting a particular applicant. The ultimate objective of the financial institution should be to maximise profit and this view is a fundamental part of the procedure presented here.
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Ustun, Berk (Tevfik Berk). "Simple linear classifiers via discrete optimization : learning certifiably optimal scoring systems for decision-making and risk assessment." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113987.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 203-221).<br>Scoring systems are linear classification models that let users make quick predictions by adding, subtracting, and multiplying a few small numbers. These models are widely used in applications where humans have traditionally made decisions because they are easy to understand and validate. In spite of extensive deployment, many scoring systems are still built using ad hoc approaches that combine statistical techniques, heuristics, and expert judgement. Such approaches impose steep trade-offs with performance, making it difficult for practitioners to build scoring systems that will be used and accepted. In this dissertation, we present two new machine learning methods to learn scoring systems from data: Supersparse Linear Integer Models (SLIM) for decision-making applications; and Risk-calibrated Supersparse Linear Integer Models (RiskSLIM) for risk assessment applications. Both SLIM and RiskSLIM solve discrete optimization problems to learn scoring systems that are fully optimized for feature selection, small integer coefficients, and operational constraints. We formulate these problems as integer programming problems and develop specialized algorithms to recover certifiably optimal solutions with an integer programming solver. We illustrate the benefits of this approach by building scoring systems for realworld problems such as recidivism prediction, sleep apnea screening, ICU seizure prediction, and adult ADHD diagnosis. Our results show that a discrete optimization approach can learn simple models that perform well in comparison to the state-of-the-art, but that are far easier to customize, understand, and validate.<br>by Berk Ustun.<br>Ph. D.
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Anantharajah, Kaneswaran. "Robust face clustering for real-world data." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/89400/1/Kaneswaran_Anantharajah_Thesis.pdf.

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This thesis has investigated how to cluster a large number of faces within a multi-media corpus in the presence of large session variation. Quality metrics are used to select the best faces to represent a sequence of faces; and session variation modelling improves clustering performance in the presence of wide variations across videos. Findings from this thesis contribute to improving the performance of both face verification systems and the fully automated clustering of faces from a large video corpus.
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Sanchez, Merchante Luis Francisco. "Learning algorithms for sparse classification." Phd thesis, Université de Technologie de Compiègne, 2013. http://tel.archives-ouvertes.fr/tel-00868847.

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This thesis deals with the development of estimation algorithms with embedded feature selection the context of high dimensional data, in the supervised and unsupervised frameworks. The contributions of this work are materialized by two algorithms, GLOSS for the supervised domain and Mix-GLOSS for unsupervised counterpart. Both algorithms are based on the resolution of optimal scoring regression regularized with a quadratic formulation of the group-Lasso penalty which encourages the removal of uninformative features. The theoretical foundations that prove that a group-Lasso penalized optimal scoring regression can be used to solve a linear discriminant analysis bave been firstly developed in this work. The theory that adapts this technique to the unsupervised domain by means of the EM algorithm is not new, but it has never been clearly exposed for a sparsity-inducing penalty. This thesis solidly demonstrates that the utilization of group-Lasso penalized optimal scoring regression inside an EM algorithm is possible. Our algorithms have been tested with real and artificial high dimensional databases with impressive resuits from the point of view of the parsimony without compromising prediction performances.
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Dyremark, Johanna, and Caroline Mayer. "Bedömning av elevuppsatser genom maskininlärning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262041.

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Betygsättning upptar idag en stor del av lärares arbetstid och det finns en betydande inkonsekvens vid bedömning utförd av olika lärare. Denna studie ämnar undersöka vilken träffsäkerhet som en automtiserad bedömningsmodell kan uppnå. Tre maskininlärningsmodeller för klassifikation i form av Linear Discriminant Analysis, K-Nearest Neighbor och Random Forest tränas och testas med femfaldig korsvalidering på uppsatser från nationella prov i svenska. Klassificeringen baseras på språk och formrelaterade attribut inkluderande ord och teckenvisa längdmått, likhet med texter av olika formalitetsgrad och grammatikrelaterade mått. Detta utmynnar i ett maximalt quadratic weighted kappa-värde på 0,4829 och identisk överensstämmelse med expertgivna betyg i 57,53 % av fallen. Dessa resultat uppnåddes av en modell baserad på Linear Discriminant Analysis och uppvisar en högre korrelation med expertgivna betyg än en ordinarie lärare. Trots pågående digitalisering inom skolväsendet kvarstår ett antal hinder innan fullständigt maskininlärningsbaserad bedömning kan realiseras, såsom användarnas inställning till tekniken, etiska dilemman och teknikens svårigheter med förståelse av semantik. En delvis integrerad automatisk betygssättning har dock potential att identifiera uppsatser där behov av dubbelrättning föreligger, vilket kan öka överensstämmelsen vid storskaliga prov till en låg kostnad.<br>Today, a large amount of a teacher’s workload is comprised of essay scoring and there is a large variability between teachers’ gradings. This report aims to examine what accuracy can be acceived with an automated essay scoring system for Swedish. Three following machine learning models for classification are trained and tested with 5-fold cross-validation on essays from Swedish national tests: Linear Discriminant Analysis, K-Nearest Neighbour and Random Forest. Essays are classified based on 31 language structure related attributes such as token-based length measures, similarity to texts with different formal levels and use of grammar. The results show a maximal quadratic weighted kappa value of 0.4829 and a grading identical to expert’s assessment in 57.53% of all tests. These results were achieved by a model based on Linear Discriminant Analysis and showed higher inter-rater reliability with expert grading than a local teacher. Despite an ongoing digitilization within the Swedish educational system, there are a number of obstacles preventing a complete automization of essay scoring such as users’ attitude, ethical issues and the current techniques difficulties in understanding semantics. Nevertheless, a partial integration of automatic essay scoring has potential to effectively identify essays suitable for double grading which can increase the consistency of large-scale tests to a low cost.
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Maldonado, Alarcón Sebastián Alejandro. "Utilización de Support Vector Machines No Lineal y Selección de Atributos para Credit Scoring." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/102885.

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Souza, Victor Hugo Delvalle. "Estimação de escores binomiais correlacionados: uma aplicação em Credit Scoring." Universidade Federal de São Carlos, 2008. https://repositorio.ufscar.br/handle/ufscar/4520.

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Made available in DSpace on 2016-06-02T20:06:01Z (GMT). No. of bitstreams: 1 1989.pdf: 231034 bytes, checksum: 31bd4dd9e300bf47b5b32ecca7d161ab (MD5) Previous issue date: 2008-06-09<br>Financiadora de Estudos e Projetos<br>For the most part of modelings in the credit risk area, the most widely used model is the credit scoring, and as the main statistical technique, the binary logistic regression, used to determine whether a customer is a good or bad payer. In this academic work an alternative methodology is proposed, where the estimative is formed based on the scores obtained by customers; this means the response follows a binomial distribution. In this modeling the combined estimate of scores of various products used by customers is included, considering the correlation between these scores.<br>Em grande parte das modelagens na área de risco de crédito, o modelo mais utilizado é o credit scoring, e como técnica estatística principal a regressão logistica binária, utilizada para decidir se um cliente é bom ou mau pagador. Neste trabalho propomos uma metodologia alternativa, onde a estimativa é feita diretamente nos escores dos clientes, com issa a resposta segue uma distribuição binomial. Nessa modelagem incluimos ainda a estimativa conjunta dos escores de vários produtos utilizados pelos clientes, levando em consideração a correlação existente entre estes escores.
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Leonard, Kevin John. "Credit scoring via linear logistic models with random parameters." Thesis, 1988. http://spectrum.library.concordia.ca/2884/1/NL44881.pdf.

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Wu, Chih-Peng, and 吳智棚. "A study on non-linear regression of the energy scoring function for molecular docking." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/24631710427100302338.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>95<br>Virtual screening by molecular docking has become a crucial component for hit identification and lead optimization against very large libraries of compounds, but there is still much room for improvement in design of scoring function. The most common problem of existing scoring functions is the existence of “outliers”. Outliers of molecular docking can be very important and interesting especially when the observed biological activity is higher than the predicted one by scoring function. This article proposes a non-linear scoring function along with outlier detection. The evaluation is conducted with a comparison against the scoring function incorporated in the well-known AutoDock docking package. Based on the testing dataset from 607 protein-ligand complexes, the proposed non-linear scoring function has RMSE (root-mean-squared-error) equal to 2.13 kcal/mol that is comparable with the scoring function in AutoDock (3.453 kcal/mol). Moreover, with the proposed outlier detection mechanism, the RMSE could improve to 2.0 kcal/mol. As a result, the proposed scoring function with outlier detection helps the scoring quality and provides valuable clues for further biochemical analysis.
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Books on the topic "Linear Scoring"

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Zeng, Lingjia. Standard errors of linear equating for the single-group design. American College Testing Program, 1991.

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Banerjee, Ashis, and Clara Oliver. Cardiac emergencies. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198786870.003.0009.

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Chest pain is a common presenting complaint for patients in the emergency department. This chapter focuses on the management and recent changes to non-ST-segment elevation myocardial infarction (NSTEMI) and STEMI pathways, in keeping with national guidance. Arrhythmia management including atrial fibrillation as well as the use of scoring systems as the CHADVASC score also commonly appears in the short-answer question (SAQ) paper, which is covered in this chapter in line with current NICE guidance. In addition, there is also a section on the diagnosis and differentiation on managing a patient with a transient loss of consciousness and the associated risk factors of sudden cardiac death. This chapter also includes sections on hypertensive emergencies and the management of heart failure.
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Hecht, Paul J. What Rosalind Likes. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192857200.001.0001.

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Abstract What Rosalind Likes begins with the strange ferocity of Elizabethan responses to poetry: a woman named Rosalind expresses scorn for a shepherd’s poems, and a character in a play loses his temper and storms off stage at the sound of a blank verse line. What are these people so angry about? Thus begins a journey into a world where the details of poetic form and vagaries of Latin translation are caught up in the dynamics of gender, sexuality, class, and race, and power, where too much alliteration, for example, could destabilize your gender or pose a threat to national security. Situated in the crucial final two decades of the sixteenth century, What Rosalind Likes takes three figures named “Rosalind” in works by Spenser (The Shepheardes Calender), Lodge (Rosalynde), and Shakespeare (As You Like It) to create a new approach to literary history and feminist criticism. The development and emergence of Rosalind as one of the most famous and beloved characters in the Shakespeare canon is thus connected to the troubled history of Virgilian reception, to tensions between aesthetics and sexual empowerment and powerlessness, to methodology associated with postcritique, including surface reading and the valorization of negative emotions, and to queer theology. The book ends by thinking about Rosalind with respect to the poetry of Mary Wroth, and examining depictions of Rosalind on stage and screen by Dora Jordan and Katharine Hepburn.
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Book chapters on the topic "Linear Scoring"

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Obertacke, U., Th Joka, and C. Neumann. "Development of a Linear Scoring System." In Adult Respiratory Distress Syndrome. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84098-2_6.

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Bengtsson, Fredrik, and jingsen Chen. "Computing Maximum-Scoring Segments in Almost Linear Time." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11809678_28.

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Guidolin, Massimo, and Manuela Pedio. "Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms." In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_5.

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AbstractThe big data revolution and recent advancements in computing power have increased the interest in credit scoring techniques based on artificial intelligence. This has found easy leverage in the fact that the accuracy of credit scoring models has a crucial impact on the profitability of lending institutions. In this chapter, we survey the most popular supervised credit scoring classification methods (and their combinations through ensemble methods) in an attempt to identify a superior classification technique in the light of the applied literature. There are at least three key insights that emerge from surveying the literature. First, as far as individual classifiers are concerned, linear classification methods often display a performance that is at least as good as that of machine learning methods. Second, ensemble methods tend to outperform individual classifiers. However, a dominant ensemble method cannot be easily identified in the empirical literature. Third, despite the possibility that machine learning techniques could fail to outperform linear classification methods when standard accuracy measures are considered, in the end they lead to significant cost savings compared to the financial implications of using different scoring models.
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Fang, Chun, Tamotsu Noguchi, Hayato Yamana, and Fuzhen Sun. "Identifying Protein Short Linear Motifs by Position-Specific Scoring Matrix." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41009-8_22.

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Adams, Niall M., Dimitris K. Tasoulis, Christoforos Anagnostopoulos, and David J. Hand. "Temporally-Adaptive Linear Classification for Handling Population Drift in Credit Scoring." In Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_15.

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Cai, Shaowei, Bohan Li, and Xindi Zhang. "Local Search for SMT on Linear Integer Arithmetic." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_12.

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AbstractSatisfiability Modulo Linear Integer Arithmetic, SMT (LIA) for short, has significant applications in many domains. In this paper, we develop the first local search algorithm for SMT (LIA) by directly operating on variables, breaking through the traditional framework. We propose a local search framework by considering the distinctions between Boolean and integer variables. Moreover, we design a novel operator and scoring functions tailored for LIA, and propose a two-level operation selection heuristic. Putting these together, we develop a local search SMT (LIA) solver called LS-LIA. Experiments are carried out to evaluate LS-LIA on benchmarks from SMTLIB and two benchmark sets generated from job shop scheduling and data race detection. The results show that LS-LIA is competitive and complementary with state-of-the-art SMT solvers, and performs particularly well on those formulae with only integer variables. A simple sequential portfolio with Z3 improves the state-of-the-art on satisfiable benchmark sets of LIA and IDL benchmarks from SMT-LIB. LS-LIA also solves Job Shop Scheduling benchmarks substantially faster than traditional complete SMT solvers.
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Siami, Mohammad, Mohammad Reza Gholamian, Javad Basiri, and Mohammad Fathian. "An Application of Locally Linear Model Tree Algorithm for Predictive Accuracy of Credit Scoring." In Model and Data Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24443-8_15.

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Desmet, Pierre. "Comparison of the Predictivity of a Neural Network with Backpropagation with Those Using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring." In Advances in Computational Management Science. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2821-7_5.

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Deszczyński, Bartosz. "Validating the Relationship Management Maturity Concept." In Firm Competitive Advantage Through Relationship Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67338-3_4.

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AbstractThis chapter operationalizes and tests the preliminary proposal of the RM maturity model in field research. The first section discusses the design of the 40-question questionnaire, which aims to verify the links between RM maturity and sustainable competitive advantage. Different classes of research biases are addressed on a semantic level, and a scoring method based on the Net Promoter Score methodology is introduced. The second section presents the analytical strategy and the design of the empirical test, encompassing the self-reporting of business respondents and machine learning analytical techniques. The product of this analysis is a set of association rules, which separate a ‘basket’ of 10 critical RM activities and approaches linked by 16 rules, typically in a reciprocal way. The third section comments on the detailed empirical results on RM maturity and sustainable competitive advantage, which are found to be much in line with business ethics. The hallmark of an ethical RM-mature firm is open internal vertical and horizontal communication, which enables the company to be a meaningful partner in the relationships with its customers and other stakeholders.
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Lappas, Pantelis Z., and Athanasios N. Yannacopoulos. "Credit Scoring." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5077-9.ch028.

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The main objective of this chapter is to propose a hybrid evolutionary feature selection approach for solving credit scoring problems subject to constraints. A hybrid scheme combining filter and wrapper-based approaches is proposed to develop an accurate credit scoring model with a high predictive performance. Initially, the minimum redundancy maximum relevance algorithm is applied to find an optimal set of features that is mutually and maximally dissimilar and can represent the response variable effectively, allowing for an ordering of features by their importance. Subsequently, an iterative procedure, where supervised machine learning algorithms such as the logistic regression and the linear-discriminant analysis are combined with an evolutionary optimization algorithm like the genetic algorithm, is applied to choose the feature subset that maximizes an appropriate classification measure according to the predefined features and subject to the predefined constraints. The performance of the proposed method is illustrated using standard credit scoring datasets.
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Conference papers on the topic "Linear Scoring"

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Guo, Yuchen, Guiguang Ding, Jungong Han, Sicheng Zhao, and Bin Wang. "Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/680.

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Recognizing unseen classes is an important task for real-world applications, due to: 1) it is common that some classes in reality have no labeled image exemplar for training; and 2) novel classes emerge rapidly. Recently, to address this task many zero-shot learning (ZSL) approaches have been proposed where explicit linear scores, like inner product score, are employed to measure the similarity between a class and an image. We argue that explicit linear scoring (ELS) seems too weak to capture complicated image-class correspondence. We propose a simple yet effective framework, called Implicit Non-linear Similarity Scoring (ICINESS). In particular, we train a scoring network which uses image and class features as input, fuses them by hidden layers, and outputs the similarity. Based on the universal approximation theorem, it can approximate the true similarity function between images and classes if a proper structure is used in an implicit non-linear way, which is more flexible and powerful. With ICINESS framework, we implement ZSL algorithms by shallow and deep networks, which yield consistently superior results.
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DEMIR-KAVUK, OZGUR, FLORIAN KRULL, MYONG-HO CHAE, and ERNST-WALTER KNAPP. "PREDICTING PROTEIN COMPLEX GEOMETRIES WITH LINEAR SCORING FUNCTIONS." In Proceedings of the 10th Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2010). IMPERIAL COLLEGE PRESS, 2010. http://dx.doi.org/10.1142/9781848166585_0002.

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Phienthrakul, Tanasanee, Tewan Santitewagun, and Narit Hnoohom. "A Linear Scoring Algorithm for Shredded Paper Reconstruction." In 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2015. http://dx.doi.org/10.1109/sitis.2015.13.

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Saia, Roberto, and Salvatore Carta. "A Linear-dependence-based Approach to Design Proactive Credit Scoring Models." In 8th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0006066701110120.

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Phandi, Peter, Kian Ming A. Chai, and Hwee Tou Ng. "Flexible Domain Adaptation for Automated Essay Scoring Using Correlated Linear Regression." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1049.

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Jiang, Ming-hui, and Xu-chuan Yuan. "Personal Credit Scoring Model of Non-linear Combining Forecast Based on GP." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.551.

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Agun, Daniel, Jinjin Shao, Shiyu Ji, Stefano Tessaro, and Tao Yang. "Privacy and Efficiency Tradeoffs for Multiword TopKSearch with Linear Additive Rank Scoring." In the 2018 World Wide Web Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3178876.3186084.

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Huang, Chien-Feng, Tsung-Nan Hsieh, Bao Rong Chang, and Chih-Hsiang Chang. "A comparative study of stock scoring using regression and genetic-based linear models." In 2011 IEEE International Conference on Granular Computing (GrC-2011). IEEE, 2011. http://dx.doi.org/10.1109/grc.2011.6122606.

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Catulay, Jeff Jojer Jones E., Maco E. Magsael, Danlord O. Ancheta, and Jefferson A. Costales. "Neural-Network Architecture Approach: An Automated Essay Scoring Using Bayesian Linear Ridge Regression Algorithm." In 2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2021. http://dx.doi.org/10.1109/iscmi53840.2021.9654801.

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Corò, Federico, Emilio Cruciani, Gianlorenzo D'Angelo, and Stefano Ponziani. "Exploiting Social Influence to Control Elections Based on Scoring Rules." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/29.

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We consider the election control problem in social networks which consists in exploiting social influence in a network of voters to change their opinion about a target candidate with the aim of increasing his chances to win (constructive control) or lose (destructive control) the election. Previous works on this problem focus on plurality voting systems and on a influence model in which the opinion of the voters about the target candidate can only change by shifting its ranking by one position, regardless of the amount of influence that a voter receives. We introduce Linear Threshold Ranking, a natural extension of Linear Threshold Model, which models the change of opinions taking into account the amount of exercised influence. In this general model, we are able to approximate the maximum score that a target candidate can achieve up to a factor of 1-1/e by showing submodularity of the objective function. We exploit this result to provide a 1/3(1-1/e)-approximation algorithm for the constructive election control problem and a 1/2(1-1/e)-approximation ratio in the destructive scenario. The algorithm can be used in arbitrary scoring rule voting systems, including plurality rule and borda count.
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Reports on the topic "Linear Scoring"

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Mizrach, Amos, Sydney L. Spahr, Ephraim Maltz, et al. Ultrasonic Body Condition Measurements for Computerized Dairy Management Systems. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568109.bard.

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The body condition (BC) score is recognized in the dairy industry as an essential tool for managing the energy reserves of the dairy cow, which is essential for sustaining optimal and efficient production over several lactations. The current use of BC scoring depends on the accuracy of subjective visual estimates, and this limits its kusefulness as a management aid in the dairy industry. A measuring tool that would frequently provide objective data on the cow's body reserves would be a major contribution to efficient dairy herd management. Ultrasonic sensors have the potential to be developed into an efficient BC measuring device, and the experimental use of such sensors for subcutaneous fat thickness (SDFT) estimates, as an indication for BC in beef cattle, supports this assumption. The purposes of this project were: 1. To compare visual BC scoring and ultrasonic fat thickness with on-line automated body weight (BW) measurements as monitors of nutritional adequacy of dairy cows at various stages of lactation. 2. To determine the effects of variation in digestive fill in early and late lactation on the accuracy of body weight measurements in lactating cows. 3. To modify an existing ultrasonic system and develop a specialized, low-cost sensor for repeatable determination of body condition scores by users with minimal training and skill. 4. To develop a standard for the assignment of body condition scores based on ultrasonic measurements of subdermal fat thickness. The procedure to execute these objectives involved: 1. Frequent measurement of BW, milk yield (MY), BC (visually scored) and subdermal fat thickness ultrasonically measured of dairy cows, and data analysis on average and individual basis. 2. Testing and selection of an appropriate special-purpose sensor, finding an optimum body location for working an ultrasonic measurement, prcessing the signals obtained, and correlating the resulting measurements with performance responses in lactating cows. Linking the ultrasonic signals to BC scores, and developing a BC scoring data acquisition system are the first steps towards fulfilling the necessary requirements for incorporating this device into an existing dairy herd management system, in order to provide the industry with a powerful managment tool. From the results obtained we could conclude that: 1. BC does not correlate with BW changes during all stages of lactation, although in general terms it does. These results were confirmed by individual cow BW and BC data obtained during the course of lactation, that were supported by individual objective ultrasonic measurement of SDFT. 2. BW changes reflect energy metabolism reliably ony after peak milk yield; early in lactation, a decrease in BW expresses mobilization of body reserves only qualitatively, and not quantitatively. 3. Gastrointestinal content increases throughout the whole period during which dry matter intake (DMI) increases. The drastic increase very early in lactation prevents the use of BW changes as a basis for quantitative estimatio of energy meatabolism; at this stage of lactation, konly a BC score or any other direct measurements willl provide a quantitative estimate of energy metabolism. 4. Ultrasonic measurements of subdermal fat thickness can be used to quantify changes that correlate with the actual condition of the cow, as assessed by performance and the traditional way of scoring. 5. To find the best site on the cow's body at which to obtain responses to BC and its changes in the course of lactation, additional sites have to be examined. From the present study, it seems that the sites between ribs 12 and 13 have the potential for this purpose. 6. The use of templates made it easier to repeat measurements at a desired site and spot. However, the convenient easy-to-handle way to standardize the measurement, described in this study, koffers scope for improvement. 7. The RF peak values of the A-mode are better indicators of the location of fat layer borders than image analysis, from the point of view of future commercial development. 8. The distances between the RF peaks of the A-mode can be automatically measured by suitable software, for future commercial development. 9. Proper analysis of daily body weight and milk yield data can provide the necessary information on body condition changes during lactation, until a direct BC measurement device is developed. 10. In any case, at least one visual BC assessment has to be done, preferably immediately after calving, for calibration purposes.
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Dick, Warren, Yona Chen, and Maurice Watson. Improving nutrient availability in alkaline coal combustion by-products amended with composted animal manures. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587240.bard.

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Hypothesis and Objectives: We hypothesized that coal combustion products (CCPs), including those created during scrubbing of sulfur dioxide from flue gases, can be used alone or mixed with composted animal manures as effective growth media for plants. Our specific objectives were, therefore, to (1) measure the chemical, physical and hydraulic properties of source materials and prepared mixes, (2) determine the optimum design mix of CCPs and composted animal manures for growth of plants, (3) evaluate the leachate water quality and plant uptake of selected elements from prepared mixes, (4) quantify the interaction between composted animal manures and B concentrations in the mixes, (5) study the availability of P to plants growing in the mixes, and (6) determine the microbial community and siderophores involved in the solubilization of Fe and its transfer to plants. Background: In recent years a major expansion of electricity production by coal combustion has taken place in Israel, the United States and the rest of the world. As a result, a large amount of CCPs are created that include bottom ash, fly ash, flue gas desulfurization (FGD) gypsum and other combustion products. In Israel 100,000 tons of fly ash (10% of total CCPs) are produced each year and in the US a total of 123 million tons of CCPs are produced each year with 71 million tons of fly ash, 18 million tons of bottom ash and 12 million tons of FGD gypsum. Many new scrubbers are being installed and will come on-line in the next 2 to 10 years and this will greatly expand the amount of FGD gypsum. One of the main substrates used in Israel for growth media is volcanic ash (scoria; tuff). The resemblance of bottom coal ash to tuff led us to the assumption that it is possible to substitute tuff with bottom ash. Similarly, bottom ash and FGD gypsum were considered excellent materials for creating growth mixes for agricultural and nursery production uses. In the experiments conducted, bottom ash was studied in Israel and bottom ash, fly ash and FGD gypsum was studied in the US. Major Achievements: In the US, mixes were tested that combine bottom ash, organic amendments (i.e. composts) and FGD gypsum and the best mixes supported growth of tomato, wheat and marigolds that were equal to or better than two commercial mixes used as a positive control. Plants grown on bottom ash in Israel also performed very well and microelements and radionuclides analyses conducted on plants grown on bottom coal ash proved it is safe to ingest the edible organs of these plants. According to these findings, approval to use bottom coal ash for growing vegetables and fruits was issued by the Israeli Ministry of Health. Implications: Bottom coal ash is a suitable substitute for volcanic ash (scoria; tuff) obtained from the Golan Heights as a growth medium in Israel. Recycling of bottom coal ash is more environmentally sustainable than mining a nonrenewable resource. The use of mixes containing CCPs was shown feasible for growing plants in the United States and is now being evaluated at a commercial nursery where red sunset maple trees are being grown in a pot-in-pot production system. In addition, because of the large amount of FGD gypsum that will become available, its use for production of agronomic crops is being expanded due to success of this study.
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Dick, Warren, Yona Chen, and Maurice Watson. Improving nutrient availability in alkaline coal combustion by-products amended with composted animal manures. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7695883.bard.

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Hypothesis and Objectives: We hypothesized that coal combustion products (CCPs), including those created during scrubbing of sulfur dioxide from flue gases, can be used alone or mixed with composted animal manures as effective growth media for plants. Our specific objectives were, therefore, to (1) measure the chemical, physical and hydraulic properties of source materials and prepared mixes, (2) determine the optimum design mix of CCPs and composted animal manures for growth of plants, (3) evaluate the leachate water quality and plant uptake of selected elements from prepared mixes, (4) quantify the interaction between composted animal manures and B concentrations in the mixes, (5) study the availability of P to plants growing in the mixes, and (6) determine the microbial community and siderophores involved in the solubilization of Fe and its transfer to plants. Background: In recent years a major expansion of electricity production by coal combustion has taken place in Israel, the United States and the rest of the world. As a result, a large amount of CCPs are created that include bottom ash, fly ash, flue gas desulfurization (FGD) gypsum and other combustion products. In Israel 100,000 tons of fly ash (10% of total CCPs) are produced each year and in the US a total of 123 million tons of CCPs are produced each year with 71 million tons of fly ash, 18 million tons of bottom ash and 12 million tons of FGD gypsum. Many new scrubbers are being installed and will come on-line in the next 2 to 10 years and this will greatly expand the amount of FGD gypsum. One of the main substrates used in Israel for growth media is volcanic ash (scoria; tuff). The resemblance of bottom coal ash to tuff led us to the assumption that it is possible to substitute tuff with bottom ash. Similarly, bottom ash and FGD gypsum were considered excellent materials for creating growth mixes for agricultural and nursery production uses. In the experiments conducted, bottom ash was studied in Israel and bottom ash, fly ash and FGD gypsum was studied in the US. Major Achievements: In the US, mixes were tested that combine bottom ash, organic amendments (i.e. composts) and FGD gypsum and the best mixes supported growth of tomato, wheat and marigolds that were equal to or better than two commercial mixes used as a positive control. Plants grown on bottom ash in Israel also performed very well and microelements and radionuclides analyses conducted on plants grown on bottom coal ash proved it is safe to ingest the edible organs of these plants. According to these findings, approval to use bottom coal ash for growing vegetables and fruits was issued by the Israeli Ministry of Health. Implications: Bottom coal ash is a suitable substitute for volcanic ash (scoria; tuff) obtained from the Golan Heights as a growth medium in Israel. Recycling of bottom coal ash is more environmentally sustainable than mining a nonrenewable resource. The use of mixes containing CCPs was shown feasible for growing plants in the United States and is now being evaluated at a commercial nursery where red sunset maple trees are being grown in a pot-in-pot production system. In addition, because of the large amount of FGD gypsum that will become available, its use for production of agronomic crops is being expanded due to success of this study.
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