scholarly journals An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment

2021 ◽  
Vol 12 ◽  
Author(s):  
Ling Ling Wang ◽  
Tao Xin ◽  
Liu Yanlou
Keyword(s):  
Bayesian Networks ◽  
Response Pattern ◽  
Classification Performance ◽  
Diagnostic Assessment ◽  
Training Method ◽  
Mcmc Algorithm ◽  
Informative Priors ◽  
Dina Model ◽  
Realistic Data ◽  
The Ideal

Bayesian networks (BNs) can be employed to cognitive diagnostic assessment (CDA). Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending (GD) learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints (greater skill should lead to better item performance) cannot be satisfied in the above two methods. This paper proposed to train the BN first based on the ideal response pattern data contained in every CDA and continue to estimate the parameters of BN based on the EM or the GD algorithm regarding the parameters based on the IRP training method as informative priors. Both the simulation study and realistic data analysis demonstrated the validity and feasibility of the new method. The BN based on the new parameter estimating method exhibits promising statistical classification performance and even outperforms the G-DINA model in some conditions.

The Gray World of Practical Data Analysis: An Introduction to Part 2

2011 ◽  
Author(s):  
Gidon Eshel
Keyword(s):  
Data Analysis ◽  
Linear System ◽  
Linear Algebra ◽  
Actual Data ◽  
Distinguishing Characteristic ◽  
Real Numbers ◽  
Ideal World ◽  
Main Distinguishing Characteristic ◽  
Realistic Data ◽  
The Ideal

This chapter provides an overview of the second part of the book. This part is the crux of the matter: how to analyze actual data. While this part builds on Part 1, especially on linear algebra fundamentals covered in Part 1, the two are not redundant. The main distinguishing characteristic of Part 2 is its nuanced grayness. In the ideal world of algebra (and thus in most of part 1), things are black or white: two vectors are either mutually orthogonal or not, real numbers are either zero or not, a vector either solves a linear system or does not. By contrast, realistic data analysis, the province of Part 2, is always gray, always involves subjective decisions.

Variational Bayes Inference for the DINA Model

2020 ◽  
Vol 45 (5) ◽  
pp. 569-597
Author(s):  
Kazuhiro Yamaguchi ◽  
Kensuke Okada
Keyword(s):  
Real Data ◽  
Variational Bayes ◽  
Diagnostic Assessment ◽  
Model Parameters ◽  
Mcmc Method ◽  
Inference Method ◽  
Bayes Inference ◽  
Dina Model ◽  
Mcmc Estimation ◽  
Parameter Values

In this article, we propose a variational Bayes (VB) inference method for the deterministic input noisy AND gate model of cognitive diagnostic assessment. The proposed method, which applies the iterative algorithm for optimization, is derived based on the optimal variational posteriors of the model parameters. The proposed VB inference enables much faster computation than the existing Markov chain Monte Carlo (MCMC) method, while still offering the benefits of a full Bayesian framework. A simulation study revealed that the proposed VB estimation adequately recovered the parameter values. Moreover, an example using real data revealed that the proposed VB inference method provided similar estimates to MCMC estimation with much faster computation.

scholarly journals Exploring Multiple Strategic Problem Solving Behaviors in Educational Psychology Research by Using Mixture Cognitive Diagnosis Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiwei Zhang ◽  
Jing Lu ◽  
Jing Yang ◽  
Zhaoyuan Zhang ◽  
Shanshan Sun
Keyword(s):  
Problem Solving ◽  
Diagnostic Testing ◽  
Cognitive Diagnosis ◽  
Mcmc Algorithm ◽  
Multiple Strategies ◽  
Dina Model ◽  
Single Strategy ◽  
Diagnosis Model ◽  
Multiple Strategy ◽  
Cognitive Diagnosis Model

A mixture cognitive diagnosis model (CDM), which is called mixture multiple strategy-Deterministic, Inputs, Noisy “and” Gate (MMS-DINA) model, is proposed to investigate individual differences in the selection of response categories in multiple-strategy items. The MMS-DINA model system is an effective psychometric and statistical approach consisting of multiple strategies for practical skills diagnostic testing, which not only allows for multiple strategies of problem solving, but also allows for different strategies to be associated with different levels of difficulty. A Markov chain Monte Carlo (MCMC) algorithm for parameter estimation is given to estimate model, and four simulation studies are presented to evaluate the performance of the MCMC algorithm. Based on the available MCMC outputs, two Bayesian model selection criteria are computed for guiding the choice of the single strategy DINA model and multiple strategy DINA models. An analysis of fraction subtraction data is provided as an illustration example.

Cognitive Diagnostic Assessment with Different Weight for Attribute: Based on the Dina Model

2014 ◽  
Vol 114 (3) ◽  
pp. 802-822
Author(s):  
Lei Guo ◽  
Yu Bao ◽  
Zhuoran Wang ◽  
Yufang Bian
Keyword(s):  
Least Squares ◽  
Diagnostic Methods ◽  
Diagnostic Assessment ◽  
Simulation Studies ◽  
Distance Method ◽  
Attribute Weight ◽  
Dina Model ◽  
Knowledge States ◽  
Diagnostic Models ◽  
Weight Calculation

An attribute weight calculation method which used a Bayesian network and the least squares distance method was proposed to assign different weights to different attributes in cognitive diagnosis. This method is independent of any specific cognitive diagnostic models, so it is practicable to consider attribute weight not only in the models with explicit expression but also in item response theory-based cognitive diagnostic methods. Simulation studies showed that the data fit for the least squares distance method was excellent and the weighted status can yield higher correct classification rates than the unweighted status. The weighted status had a promising performance in recognizing the knowledge states of examinees for various slippage probabilities under different attribute hierarchies. The numbers of items and attributes could also affect the examinees' classification accuracy.

scholarly journals Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Gonzalo A. Ruz ◽  
Pamela Araya-Díaz
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Classification Problem ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Bayesian Network Classifiers ◽  
Tree Construction ◽  
Learning Techniques ◽  
Vector Machines

Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.

A model to estimate the Self-Organizing Maps grid dimension for Prototype Generation

2021 ◽  
Vol 25 (2) ◽  
pp. 321-338
Author(s):  
Leandro A. Silva ◽  
Bruno P. de Vasconcelos ◽  
Emilio Del-Moral-Hernandez
Keyword(s):  
Nearest Neighbor ◽  
High Accuracy ◽  
Classification Performance ◽  
K Nearest Neighbor ◽  
Self Organizing Maps ◽  
Ideal Number ◽  
K Nearest Neighbor Algorithm ◽  
Public Datasets ◽  
The Ideal ◽  
Self Organizing

Due to the high accuracy of the K nearest neighbor algorithm in different problems, KNN is one of the most important classifiers used in data mining applications and is recognized in the literature as a benchmark algorithm. Despite its high accuracy, KNN has some weaknesses, such as the time taken by the classification process, which is a disadvantage in many problems, particularly in those that involve a large dataset. The literature presents some approaches to reduce the classification time of KNN by selecting only the most important dataset examples. One of these methods is called Prototype Generation (PG) and the idea is to represent the dataset examples in prototypes. Thus, the classification process occurs in two steps; the first is based on prototypes and the second on the examples represented by the nearest prototypes. The main problem of this approach is a lack of definition about the ideal number of prototypes. This study proposes a model that allows the best grid dimension of Self-Organizing Maps and the ideal number of prototypes to be estimated using the number of dataset examples as a parameter. The approach is contrasted with other PG methods from the literature based on artificial intelligence that propose to automatically define the number of prototypes. The main advantage of the proposed method tested here using eighteen public datasets is that it allows a better relationship between a reduced number of prototypes and accuracy, providing a sufficient number that does not degrade KNN classification performance.

scholarly journals REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS

2011 ◽  
Vol 21 (02) ◽  
pp. 167-190 ◽  
Author(s):  
ISABEL MARÍA DEL ÁGUILA ◽  
JOSÉ DEL SAGRADO
Keyword(s):  
Bayesian Networks ◽  
Requirement Engineering ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Risk Level ◽  
Software Project ◽  
Development Activity ◽  
Learning Techniques ◽  
Risks Assessment ◽  
Requirement Specifications

Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.

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