Comparison of Computer Scoring Model Performance for Short Text Responses Across Undergraduate Institutional Types

2021 ◽  
Author(s):  
Megan Shiroda ◽  
Juli D. Uhl ◽  
Mark Urban-Lurain ◽  
Kevin C. Haudek
Keyword(s):  
Model Performance ◽  
Short Text ◽  
Scoring Model ◽  
Computer Scoring ◽  
Institutional Types

scholarly journals Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea

2021 ◽  
Vol 14 (1) ◽  
pp. 130
Author(s):  
Sunghyon Kyeong ◽  
Daehee Kim ◽  
Jinho Shin
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Credit Scoring ◽  
Model Performance ◽  
Sampled Data ◽  
Discrimination Power ◽  
Log Data ◽  
Scoring Model ◽  
Internet Bank ◽  
Credit Scoring Model

The credit scoring model is one of the most important decision-making tools for the sustainability of banking systems. This study is the first to examine whether it can be improved by using system log data that are stoed extensively for system operation. We used the log data recorded by the mobile application system of KakaoBank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from KakaoBank’s log data, we created a credit scoring model by utilizing variables with high information values and logistic regression, the most common method for developing credit scoring models in financial institutions. To prove our hypothesis on the improvement of credit scoring model performance, we performed an independent sample t-test using the simulation results of repeated model development and performance measurement based on randomly sampled data. Consequently, the discrimination power of the proposed model using logistic regression (neural network) compared to the credit bureau-based model significantly improved by 1.84 (2.22) percentage points based on the Kolmogorov–Smirnov statistics. The results of this study suggest that a bank can utilize the accumulated log data inside the bank to improve decision-making systems, including credit scoring, at a low cost.

scholarly journals Enhancing credit scoring model performance by a hybrid scoring matrix

2013 ◽  
Vol 7 (18) ◽  
pp. 1791-1805
Author(s):  
Chi Bo Wen ◽  
Hsu Chiun Chieh ◽  
Ho Mei Hung
Keyword(s):  
Credit Scoring ◽  
Model Performance ◽  
Scoring Model ◽  
Scoring Matrix ◽  
Credit Scoring Model

Building a Credit Scoring Model Based on Data Mining Approaches

2020 ◽  
Vol 30 (02) ◽  
pp. 147-169
Author(s):  
Jasmina Nalić ◽  
Goran Martinovic
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Banking Sector ◽  
Credit Scoring ◽  
Model Performance ◽  
Real Data ◽  
Support Vector ◽  
Scoring Model ◽  
Adopted Model ◽  
Credit Scoring Model

Nowadays, one of the biggest challenges in banking sector, certainly, is assessment of the client’s creditworthiness. In order to improve the decision-making process and risk management, banks resort to using data mining techniques for hidden patterns recognition within a wide data. The main objective of this study is to build a high-performance customized credit scoring model. The model named Reliable client is based on Bank’s real dataset and originally built by applying four different classification algorithms: decision tree (DT), naive Bayes (NB), generalized linear model (GLM) and support vector machine (SVM). Since it showed the greatest results, but also seemed as the most appropriate algorithm, the adopted model is based on GLM algorithm. The results of this model are presented based on many performance measures that showed great predictive confidence and accuracy, but we also demonstrated significant impact of data pre-processing on model performance. Statistical analysis of the model identified the most significant parameters on the model outcome. In the end, created credit scoring model was evaluated using another set of real data of the same Bank.

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

КОМУНІКАТИВНА КОМПЕТЕНТНІСТЬ У ПОЛІТИЧНОМУ ДИСКУРСІ (НА ПРИКЛАДІ ОФІЦІЙНИХ СТОРІНОК НАРОДНИХ ДЕПУТАТІВ У СОЦІАЛЬНІЙ МЕРЕЖІ «ФЕЙСБУК»)

2020 ◽  
pp. 4
Author(s):  
T. Sashchuk
Keyword(s):  
Social Network ◽  
Subject Matter ◽  
Communicative Competence ◽  
Professional Activity ◽  
First Person ◽  
Short Text ◽  
Scientific Methods ◽  
Content Analysis Method ◽  
General Scientific ◽  
Facebook Pages

<div><em>The article presents the results of the study of the communicative competence of the politicians on the basis of the analysis of their messages on their official pages of the Facebook social network. The research used the following general scientific methods: descriptive and comparative, as well as analysis, synthesis and generalization. The quantitative content analysis method with qualitative elements was used to distinguish the peculiarities of information messages that provide communication of the deputies of Verkhovna Rada (Ukrainian Parliament) on their official Facebook pages. Information messages have been analyzed by the following three criteria: subject matter, structure and language.</em></div><p> </p><p><em>For the first time the article draws a parallel between communicative competence and the ability to communicate with voters on the official pages of Facebook which is the most popular social network in Ukraine. As it is established, communicative competence in the analyzed cases is caused not by education, but by previous professional activity of a politician. The most successful and high-quality communication was from the current parliamentarian who worked as a journalist in the past. More than half of the messages that provided successful communication consisted of sufficiently structured short text and a video. The topic covers the activity of the parliamentarian in the Verkhovna Rada and in his district. More than half of the messages are spoken in the first person.</em></p><p><em>The findings of the study can be used in teaching such subjects as Political PR and Electronic PR, and may be of interest to politicians and their assistants.</em><em></em></p><p><strong><em>Key words:</em></strong><em> competence and competency, communicative competence, political discourse, official page of the deputy of Verkhovna Rada of Ukraine on the Facebook social network, subject matter and structure of the information message, first-person narrative, correspondence of communication to the level of communicative competence.</em></p>

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

A Study on Bestseller Short Text Semantics Analysis Using Topic Model

2018 ◽  
Vol 15 ◽  
pp. 101-112
Author(s):  
So-Hyun Park ◽  
Ae-Rin Song ◽  
Young-Ho Park ◽  
Sun-Young Ihm
Keyword(s):  
Topic Model ◽  
Short Text

Leaching Kinetics of Atrazine and Inorganic Chemicals in Tilled and Orchard Soils

2014 ◽  
Vol 28 (2) ◽  
pp. 231-237 ◽  
Author(s):  
Lech W. Szajdak ◽  
Jerzy Lipiec ◽  
Anna Siczek ◽  
Artur Nosalewicz ◽  
Urszula Majewska
Keyword(s):  
Reaction Rate ◽  
Inorganic Compounds ◽  
Model Performance ◽  
Order Reaction ◽  
First Order Reaction ◽  
Order Kinetic ◽  
Time Data ◽  
First Order ◽  
Concentration Changes ◽  
Reaction Constants

Abstract The aim of this study was to verify first-order kinetic reaction rate model performance in predicting of leaching of atrazine and inorganic compounds (K+1, Fe+3, Mg+2, Mn+2, NH4 +, NO3 - and PO4 -3) from tilled and orchard silty loam soils. This model provided an excellent fit to the experimental concentration changes of the compounds vs. time data during leaching. Calculated values of the first-order reaction rate constants for the changes of all chemicals were from 3.8 to 19.0 times higher in orchard than in tilled soil. Higher first-order reaction constants for orchard than tilled soil correspond with both higher total porosity and contribution of biological pores in the former. The first order reaction constants for the leaching of chemical compounds enables prediction of the actual compound concentration and the interactions between compound and soil as affected by management system. The study demonstrates the effectiveness of simultaneous chemical and physical analyses as a tool for the understanding of leaching in variously managed soils.

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