Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional processes are themselves evolutionary practices of mutual learning in which actors, regardless of their initial political positions, continuously interact with each other, demonstrating differences and making alliances regarding different topics. In this article, we develop Tree Augmented Naive Bayes (TAN) classifiers to model the behavior of constituent agents. According to the nature of the constituent dynamics, weights are learned by the model from the data using an evolution strategy to obtain a good classification performance. For our analysis, we used the constituent agents’ communications on Twitter during the installation period of the Constitutional Convention (July–October 2021). In order to differentiate political positions (left, center, right), we applied the developed algorithm to obtain the scores of 882 ballots cast in the first stage of the convention (4 July to 29 September 2021). Then, we used k-means to identify three clusters containing right-wing, center, and left-wing positions. Experimental results obtained using the three constructed datasets showed that using alternative weight values in the TAN construction procedure, inferred by an evolution strategy, yielded improvements in the classification accuracy measured in the test sets compared to the results of the TAN constructed with conditional mutual information, as well as other Bayesian network classifier construction approaches. Additionally, our results may help us to better understand political behavior in constitutional processes and to improve the accuracy of TAN classifiers applied to social, real-world data.

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A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier

Intelligent Data Analysis ◽

10.3233/ida-194959 ◽

2021 ◽

Vol 25 (1) ◽

pp. 35-55

Author(s):

Limin Wang ◽

Peng Chen ◽

Shenglei Chen ◽

Minghui Sun

Keyword(s):

Bayesian Network ◽

Conditional Independence ◽

Structure Learning ◽

Classification Performance ◽

Data Sets ◽

Independence Assumption ◽

Bayesian Network Classifiers ◽

Novel Approach ◽

Conditional Independence Assumption ◽

Dependence Criterion

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

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Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

Complexity ◽

10.1155/2018/4075656 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

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.

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Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier

Entropy ◽

10.3390/e20120897 ◽

2018 ◽

Vol 20 (12) ◽

pp. 897 ◽

Cited By ~ 4

Author(s):

Yang Liu ◽

Limin Wang ◽

Minghui Sun

Keyword(s):

Bayesian Network ◽

Structure Learning ◽

State Of The Art ◽

Classification Performance ◽

Bayesian Classifier ◽

Bayesian Network Classifiers ◽

Discriminative Model ◽

Minimal Redundancy ◽

Structure Complexity ◽

Maximal Relevance

The rapid growth in data makes the quest for highly scalable learners a popular one. To achieve the trade-off between structure complexity and classification accuracy, the k-dependence Bayesian classifier (KDB) allows to represent different number of interdependencies for different data sizes. In this paper, we proposed two methods to improve the classification performance of KDB. Firstly, we use the minimal-redundancy-maximal-relevance analysis, which sorts the predictive features to identify redundant ones. Then, we propose an improved discriminative model selection to select an optimal sub-model by removing redundant features and arcs in the Bayesian network. Experimental results on 40 UCI datasets demonstrate that these two techniques are complementary and the proposed algorithm achieves competitive classification performance, and less classification time than other state-of-the-art Bayesian network classifiers like tree-augmented naive Bayes and averaged one-dependence estimators.

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Bagging k-dependence Bayesian network classifiers

Intelligent Data Analysis ◽

10.3233/ida-205125 ◽

2021 ◽

Vol 25 (3) ◽

pp. 641-667

Author(s):

Limin Wang ◽

Sikai Qi ◽

Yang Liu ◽

Hua Lou ◽

Xin Zuo

Keyword(s):

Machine Learning ◽

Bayesian Network ◽

Experimental Evaluation ◽

State Of The Art ◽

Classification Performance ◽

Bayesian Classifier ◽

Bayesian Network Classifiers ◽

Log Likelihood ◽

Simple Implementation ◽

Classifier Diversity

Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.

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Structure Learning of Bayesian Network Based on Adaptive Thresholding

Entropy ◽

10.3390/e21070665 ◽

2019 ◽

Vol 21 (7) ◽

pp. 665

Author(s):

Yang Zhang ◽

Limin Wang ◽

Zhiyi Duan ◽

Minghui Sun

Keyword(s):

Bayesian Network ◽

Structure Learning ◽

Mean Squared Error ◽

Structural Complexity ◽

Classification Performance ◽

Adaptive Thresholding ◽

Bayesian Network Classifiers ◽

Structure Reliability ◽

Adaptive Thresholds ◽

Error Bias

Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant dependencies one by one, whereas the high computational overheads make them inefficient especially for those BNCs with high structural complexity. Study of the distributions of information-theoretic measures provides a feasible approach to identifying non-significant dependencies in batch that may help increase the structure reliability and avoid overfitting. In this paper, we investigate two extensions to the k-dependence Bayesian classifier, MI-based feature selection, and CMI-based dependence selection. These two techniques apply a novel adaptive thresholding method to filter out redundancy and can work jointly. Experimental results on 30 datasets from the UCI machine learning repository demonstrate that adaptive thresholds can help distinguish between dependencies and independencies and the proposed algorithm achieves competitive classification performance compared to several state-of-the-art BNCs in terms of 0–1 loss, root mean squared error, bias, and variance.

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Peranan Keluarga dalam Pendidikan Emosional Anak

INSANIA Jurnal Pemikiran Alternatif Kependidikan ◽

10.24090/insania.v13i3.307 ◽

1970 ◽

Vol 13 (3) ◽

pp. 426-440

Author(s):

Yuni Setia Ningsih

Keyword(s):

Social Order ◽

Social Life ◽

World View ◽

Early Age ◽

Way Of Thinking ◽

Family Condition ◽

Emotional Education

Family is a tiny scope that will bring someone to social life. The fine social order influenced by condition of every family inside it, because society is an accumulation and reflection of lifestyle, world view, even way of thinking of every individual in a family. Good or worse community at social life is depending on family condition. Family is playing important role to direct children to become good moral generation on and beneficial for society. Therefore, to realize that goal, children emotional education from early age at family scope is requirement.

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A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190115155223 ◽

2020 ◽

Vol 16 (3) ◽

pp. 199-205

Author(s):

Yuejun Liu ◽

Yifei Xu ◽

Xiangzheng Meng ◽

Xuguang Wang ◽

Tianxu Bai

Keyword(s):

Deep Learning ◽

Learning Algorithms ◽

Region Of Interest ◽

Classification Performance ◽

Thyroid Diseases ◽

Great Success ◽

Learning Models ◽

Good Classification Performance ◽

Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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A Unified View of Causal and Non-causal Feature Selection

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3436891 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1-46

Author(s):

Kui Yu ◽

Lin Liu ◽

Jiuyong Li

Keyword(s):

Feature Selection ◽

Bayesian Network ◽

Synthetic Data ◽

Selection Methods ◽

Bayesian Network Model ◽

Real World Data ◽

Feature Sets ◽

Unified View ◽

Optimal Feature ◽

Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

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The Mission of the Catholic Church for the State in the Discourse of “Gosc Niedzielny” during Poland’s Interwar Period

Religions ◽

10.3390/rel12030188 ◽

2021 ◽

Vol 12 (3) ◽

pp. 188

Author(s):

Rafał Śpiewak ◽

Wiktor Widera

Keyword(s):

Discourse Analysis ◽

Catholic Church ◽

Social Order ◽

Social Life ◽

Common Good ◽

The State ◽

Modern World ◽

Level Of Involvement ◽

Historical Reflection ◽

The Catholic Church

The essence of the Catholic Church implemented in the modern world is of crucial importance for the understanding its mission towards the state, especially when developing appropriate civil attitudes. One sources of cognition is the historical reflection made on an analytical basis of Catholic media content. This article presents the discourse analysis of Gość Niedzielny (i.e., Sunday Guest), which was one of the most important Catholic publications in Poland, during the reconstruction of the Polish statehood. The pro-state mission of the Catholic Church was an expression of responsibility for common good, was nonpartisan and was connected with the promotion of values that condition the social order. It was believed that the condition of the state is determined by the moral form of its citizens and their level of involvement in social life. Christian values were though to secure and protect also the good of non-Catholic citizens. Here, the research and discourse analysis allows us to define the conclusions regarding contemporary relations between Church and the state in Poland. The key thoughts included in the publications of Sunday Guest, have contemporary application and their message is extremely up-to-date.

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Structure Extension of Tree-Augmented Naive Bayes

Entropy ◽

10.3390/e21080721 ◽

2019 ◽

Vol 21 (8) ◽

pp. 721 ◽

Cited By ~ 1

Author(s):

YuGuang Long ◽

LiMin Wang ◽

MingHui Sun

Keyword(s):

Naive Bayes ◽

Classification Performance ◽

Naïve Bayes ◽

Training Data ◽

Conditional Probability Distribution ◽

Independence Assumption ◽

Bayesian Network Classifiers ◽

Leibler Divergence ◽

The Difference ◽

Structure Extension

Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler divergence, the difference between NB and its variations lies in different orders of conditional mutual information represented by these augmenting edges in the tree-shaped network structure. In this paper, we propose to relax the independence assumption by further generalizing tree-augmented naive Bayes (TAN) from 1-dependence Bayesian network classifiers (BNC) to arbitrary k-dependence. Sub-models of TAN that are built to respectively represent specific conditional dependence relationships may “best match” the conditional probability distribution over the training data. Extensive experimental results reveal that the proposed algorithm achieves bias-variance trade-off and substantially better generalization performance than state-of-the-art classifiers such as logistic regression.

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