Classification Rules Research Articles

Analysis of Disciplinary Characteristics of Elementary School Students: A Survey Research

This research aims to analyze the level of student discipline character in elementary schools at the Aceh Besar Regency level. This research uses a quantitative approach with a survey research type. The population of this study were elementary school students at the Aceh Besar Regency level with a research sample of 90 students from four different schools. The sampling technique used in this research was Cluster random sampling. This research data was collected using a questionnaire. The data obtained is quantitative data which was analyzed using descriptive statistical techniques. The results of this research show that students' disciplinary attitudes are fairly good, where the strategy carried out by the school to improve the formation of disciplinary character is the habit of activities that form disciplinary character. The habits include time discipline and how to dress. The implementation of school rules and regulations in improving the disciplinary character of elementary school students in Aceh Besar Regency has instilled a disciplinary character through religious activities and activities related to disciplinary character such as haircut rules, time discipline, Duha prayers. All these activities are carried out by the school through the habituation method. Student discipline in school grows because teachers provide motivation, create class rules, and reward students who are always on time.

Implementation Of Educational Discipline In Synergy Between Parent and Teacher In Schooling In Babussalam Lombok East SMK

The purpose of this research is to further describe how the implementation of the character education discipline students. The type of research used is a qualitative approach that uses descriptive methods, data collection techniques that are observations, interviews and documentation. Primary data sources are obtained from observations and interviews with teachers, head of school and some parents of pupils, and primary data resources are derived from previous research journals that investigate disciplinary character education. The results of the research showed that there are four ways that schools are trying to support student character education, namely, performing character education programming at school, setting up school rules and class rules, monitoring student disciplinary behavior at home Through the recording of daily activities and the involvement of parents in supporting the character education programme of this discipline by the school.

Patrol Boat Strengthening Against a Collision with COLL Notation Based on Class Rules and Regulation in Indonesia – An Overview

<span lang="EN-US">Indonesian maritime security and law enforcement rely frequently on Patrol boats. However, collisions can occur during the operation, leading to potential loss of life, damage to the ship, and environmental harm. In preventing such incidents, the government needs to strengthen the patrol boats against collisions in accordance with class rules and regulations in Indonesia.</span><span lang="EN-US">The COLL notation is an additional notation for vessel collision protection, which specifies the required strength of the vessel's hull and structural components to minimize the risk of damage and reduce the consequences of a collision. This study highlights the key areas that require strengthening, including the vessel’s bow, stern, and hull, as well as the propulsion system that needs to be considered in the design stage. The addition of collision bulkheads, increasing the hull plating thickness, and reinforcing the engine mounts and shafting are also necessary to ensure the vessel's safety against collision. In conclusion, strengthening patrol boats against a collision with COLL notation based on class rules and regulations in Indonesia is one of the methods available that can be applied for the design stage to increase the level of operational safety of patrol boats.</span>

Training neural networks with classification rules for incorporating domain knowledge

Neural networks are capable of learning complex concepts and tasks, given abundant training data. In real-world applications where data collection can be difficult, integrating domain knowledge into the model can reduce the burden on data requirements and allow human experts greater control over model decisions. This paper focuses on incorporating conditional statements for tabular data as classification rules, which have a simple structure and are easy to construct. We introduce a general rule loss constraint to guide neural network training in a model agnostic manner, and propose confidence learning to automatically weigh the contribution of multiple candidate rules. Experimental evaluation with three real-world datasets shows that the rule loss can substantially increase model performance, particularly when training data is limited.

Optimal control under uncertainty: Application to the issue of CAT bonds

We propose a general framework for studying optimal issue of CAT bonds in the presence of uncertainty on the parameters. In particular, the intensity of arrival of natural disasters is inhomogeneous and may depend on unknown parameters. Given a prior on the distribution of the unknown parameters, we explain how it should evolve according to the classical Bayes rule. Taking these progressive prior-adjustments into account, we characterize the optimal policy through a quasi-variational parabolic equation, which can be solved numerically. We provide examples of application in the context of hurricanes in Florida.

Refined Characterizations of Approval-Based Committee Scoring Rules

In approval-based committee (ABC) elections, the goal is to select a fixed-size subset of the candidates, a so-called committee, based on the voters' approval ballots over the candidates. One of the most popular classes of ABC voting rules are ABC scoring rules, for which voters give points to each committee and the committees with maximal total points are chosen. While the set of ABC scoring rules has recently been characterized in a model where the output is a ranking of all committees, no full characterization of these rules exists in the standard model where a set of winning committees is returned. We address this issue by characterizing two important subclasses of ABC scoring rules in the standard ABC election model, thereby both extending the result for ABC ranking rules to the standard setting and refining it to subclasses. In more detail, by relying on a consistency axiom for variable electorates, we characterize (i) the prominent class of Thiele rules and (ii) a new class of ABC voting rules called ballot size weighted approval voting. Based on these theorems, we also infer characterizations of three well-known ABC voting rules, namely multi-winner approval voting, proportional approval voting, and satisfaction approval voting.

LR-XFL: Logical Reasoning-Based Explainable Federated Learning

Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules through a proper logical connector that is derived based on properties of client data, without requiring access to the raw data. In addition, the server also aggregates the local model updates with weight values determined by the quality of the clients’ local data as reflected by their uploaded logic rules. The results show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and 5.41% in terms of classification accuracy, rule accuracy and rule fidelity, respectively. The explicit rule evaluation and expression under LR-XFL enable human experts to validate and correct the rules on the server side, hence improving the global FL model’s robustness to errors. It has the potential to enhance the transparency of FL models for areas like healthcare and finance where both data privacy and explainability are important.

Learning to Pivot as a Smart Expert

Linear programming has been practically solved mainly by simplex and interior point methods. Compared with the weakly polynomial complexity obtained by the interior point methods, the existence of strongly polynomial bounds for the length of the pivot path generated by the simplex methods remains a mystery. In this paper, we propose two novel pivot experts that leverage both global and local information of the linear programming instances for the primal simplex method and show their excellent performance numerically. The experts can be regarded as a benchmark to evaluate the performance of classical pivot rules, although they are hard to directly implement. To tackle this challenge, we employ a graph convolutional neural network model, trained via imitation learning, to mimic the behavior of the pivot expert. Our pivot rule, learned empirically, displays a significant advantage over conventional methods in various linear programming problems, as demonstrated through a series of rigorous experiments.

EmFORE: Learning Email Folder Classification Rules by Demonstration

Tools that help with email folder management are limited, as users have to manually write rules to assign emails to folders. We present EMFORE, an iterative learning system that automatically learns and updates such rules from observations. EMFORE is fast enough to suggest and update rules in real time and suppresses mails with low confidence to reduce the number of false positives. EMFORE can use different rule grammars, and thus be adapted to different clients, without changing the user experience. Previous methods do not learn rules, require complete retraining or multiple new examples after making a mistake, and do not distinguish between inbox and other folders. EMFORE learns rules incrementally and can make the neutral decision of leaving emails in the inbox, making it an ideal candidate for integration in email clients.

Development of a fuzzy logical model of Sugeno for the classification of grain varieties

Ambiguity and fuzziness are characteristic of many real-life situations, including the classification of objects based on their characteristics. In this context, fuzzy logic is a powerful tool for modeling uncertainty and making decisions under fuzzy conditions. The Sugeno model, a subset of fuzzy logic, offers a simple and effective approach to constructing rule-based control and classification systems based on linguistic variables. This paper explores the application of the Sugeno model to classify grain varieties based on their characteristics. For this purpose, a data set containing information about the parameters of grains from three different varieties of wheat is used. The study develops a fuzzy logic model that can effectively and accurately classify grain varieties based on their size and shape. To build the model, the scikit-fuzzy library is used, which provides tools for working with fuzzy logic in the Python programming language. Experiments are conducted with different variants of classification rules, optimizing the model to achieve the highest classification accuracy and reliability. The results obtained allow us to evaluate the effectiveness and applicability of the Sugeno model for the classification problems of grain varieties. The developed model can be useful for agronomists and agricultural specialists to automate the process of identifying wheat varieties based on their characteristics.

The Build-Up Construction for Codes over a Commutative Non-Unitary Ring of Order 9

The build-up method is a powerful class of propagation rules that generate self-dual codes over finite fields and unitary rings. Recently, it was extended to non-unitary rings of order 4, to generate quasi self-dual codes. In the present paper, we introduce three such propagation rules to generate self-orthogonal, self-dual and quasi self-dual codes over a special non-unitary ring of order 9. As an application, we classify the three categories of codes completely in length at most 3, and partially in lengths 4 and 5, up to monomial equivalence.

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

Feedback and requests by occupants are relevant sources of data to improve building management, and building maintenance. Indeed, most predictable faults can be directly identified by occupants and communicated to facility managers through communications written in the end-users’ native language. In this sense, natural language processing methods can support the request identification and attribution process if they are robust enough to extract useful information from these unstructured textual sources. Machine learning (ML) can support assessing and managing these data, especially in the case of many simultaneous communications. In this field, the application of pre-processing and ML methods to English-written databases has been widely provided, while efforts in other native languages are still limited, impacting the real applicability. Moreover, the performance of combinations of methods for pre-processing, ML and classification classes attribution, has been limitedly investigated while comparing different languages. To fill this gap, this work hence explores the performance of automatic priority assignment of maintenance end-users’ requests depending on the combined influence of: (a) different natural language pre-processing methods, (b) several supervised ML algorithms, (c) two priority classification rules (2-class versus 4-class), (d) the database language (i.e. the original database written in Italian, the native end-users’ language; a translated database version in English, as standard reference). Analyses are performed on a database of about 12000 maintenance requests written in Italian concerning a stock of 23 buildings open to the public. A random sample of the sentences is supervised and labelled by 20 expert annotators following the best-worst method to attribute a priority score. Labelled sentences are then pre-processed using four different approaches to progressively reduce the number of unique words (potential predictors). Five different consolidated ML methods are applied, and comparisons involve accuracy, precision, recall and F1-score for each combination of pre-processing action, ML method and the number of priority classes. Results show that, within each ML algorithm, different pre-processing methods limitedly impact the final accuracy and average F1-score. In both Italian and English conditions, the best performance is obtained by NN, LR, SVM methods, while NB generally fails, and by considering the 2-class priority classification scale. In this sense, results confirm that facility managers can be effectively supported by ML methods for preliminary priority assessments in building maintenance processes, even when the requests database is written in end-users’ native language.

An enquiry into the role of classroom management in enhancing learners’ social skills

Learners’ social skills enhancement is one of the key components of the classroom and the basic obligation of a teacher and school. Thus, it is imperative to manage the classroom in a way that can offer a comfortable teaching-learning environment to learners. Therefore, the study aimed to examine to role of classroom management in enhancing learners' social skills. The interpretive research design was utilized to describe the qualitative data, and a generic qualitative research approach was employed to undertake the study. A total of 18 teachers from six secondary schools were selected by using a purposive sampling technique. It was instituted that classroom management and the social environment of the classroom play a vital role in learners' social skills enhancement. Based on research findings, physical resources, teacher role, class rule, conduct and regulation, socioeconomic status of learners, family background, and poverty are the leading factors that have a strapping role on learners’ skills enhancement. The selected schools to accomplish research are lacking resources, however, teachers are considered as a core in teaching-learning. It is, therefore, obligatory to provide resources to classrooms, skills to teachers, and address learners' socioeconomic status to achieve educational goals and enhance social skills.

Gender Sensitivity of Teacher Education Curricula in the Republic of Croatia

Abstract The research aims to determine whether and to what extent teacher education curricula in Croatia are gender sensitive and to examine the status of courses dealing with gender-related topics. For this purpose, a qualitative and quantitative analysis of the seven teacher education curricula was conducted. The analysis was based on Bernstein’s theory of classification and framing rules, which explains how control and power over knowledge and curriculum are exercised by selecting knowledge (from scientific disciplines) for the curriculum, design rules, and curriculum implementation. The findings indicate that gender-based knowledge is not given high status and significance as a teaching subject. Gender-related topics are included in teacher education curricula by weak framing and classification rules. While there are several advantages to such a curriculum design, weak classification and framing rules can lead to a lack of compliance in education on gender-related topics. A vague structure can interfere with students’ understanding of basic concepts and their interconnectedness. Students can choose courses based on personal interest rather than necessity and importance. Recommendations are made to achieve a higher level of gender sensitivity in teacher education curricula, which would contribute to better preparation of teachers for the education of children on gender equality.

Assessing Ecogeographic Rules in Two Sigmodontine Rodents along an Elevational Gradient in Central Chile.

Bergmann's and Allen's rules are two classic ecogeographic rules concerning the physiological mechanisms employed by endotherm vertebrates for heat conservation in cold environments, which correlate with adaptive morphological changes. Thus, larger body sizes (Bergmann's rule) and shorter appendages and limbs (Allen's rule) are expected in mammals inhabiting cold environments (higher latitudes). Both rules may also apply to elevational gradients, due to the decrease in external temperature as elevation increases. In this study, we evaluated whether these patterns were true in two coexisting sigmodontine rodents across an elevational gradient in central Chile. We analyzed whether the size of the skull, body, and appendages of Abrothrix olivacea (n = 70) and Phyllotis darwini (n = 58) correlated with elevation, as predicted by these rules in a range between 154 and 2560 m. Our data revealed weak support for the Bergmann and Allen predictions. Moreover, we observed opposite patterns when expectations of Bergmann's rules were evaluated, whereas Allen's rule just fitted for ear size in both rodent species. Our results suggest that morphological changes (cranial, body, and appendage sizes) may play a minor role in the thermoregulation of these two species at high elevations, although behavioral strategies could be more critical. Other ecological and environmental variables could explain the morphological trends observed in our study. These hypotheses should be assessed in future studies to consider the relative contribution of morphology, behavior, and physiological mechanisms to the thermal adaptation of these two rodent species at high elevations.

Classification, Regression, and Survival Rule Induction with Complex and M-of-N Elementary Conditions

Most rule induction algorithms generate rules with simple logical conditions based on equality or inequality relations. This feature limits their ability to discover complex dependencies that may exist in data. This article presents an extension to the sequential covering rule induction algorithm that allows it to generate complex and M-of-N conditions within the premises of rules. The proposed methodology uncovers complex patterns in data that are not adequately expressed by rules with simple conditions. The novel two-phase approach efficiently generates M-of-N conditions by analysing frequent sets in previously induced simple and complex rule conditions. The presented method allows rule induction for classification, regression and survival problems. Extensive experiments on various public datasets show that the proposed method often leads to more concise rulesets compared to those using only simple conditions. Importantly, the inclusion of complex conditions and M-of-N conditions has no statistically significant negative impact on the predictive ability of the ruleset. Experimental results and a ready-to-use implementation are available in the GitHub repository. The proposed algorithm can potentially serve as a valuable tool for knowledge discovery and facilitate the interpretation of rule-based models by making them more concise.

Adaptive fuzzy-evidential classification based on association rule mining

As one of the most promising classification approaches, association classification (AC) integrates data classification and association discovery techniques for generating a compact set of classification association rules. Recently, the fuzzy set and evidence theories are successively applied into AC in order to improve the classification performance in terms of accuracy and interpretability. However, from the perspectives of applicability and universality, there still exists two important issues in the current AC framework. On one hand, several key parameters, such as the number of fused rules in classification and the minimum support threshold in association discovery, are difficult to be accurately predefined in practice. On the other hand, the fixed grid-based fuzzy partition is not benefit to adapt for those datasets with large number of features. In this paper, an association rule-based adaptive fuzzy-evidential classification framework (AR-AFEC) is developed for overcoming the above limitations. To do so, an optimal rule fusion strategy and a dynamic minimum support threshold setting scheme are proposed for adaptively learning the parameters during classification and association mining respectively. In addition, an entropy-based trapezoidal fuzzy partition technique is proposed to adaptively obtain the fuzzy sets defined on each continuous feature domain. Experiments on 26 benchmark datasets and a human activity recognition application demonstrate that the proposal can achieve better accuracy than some state-of-the-art rule-based classification approaches, using less rules with more general structure.

Controversy of Copyright Application and the China Plan of Metaverse Products

Abstract The controversy over copyright theory in the metaverse scenario mainly arises from the issue of copyrightability of metaverse products. Metaverse products have initially reached the standard of originality and formally conform to the dichotomy of thought and expression, however, the incompatibility of object types has led to controversy over its copyright theory. In addition, the three types of disputes and difficulties based on the “behavior” element: original acquisition and derivative acquisition, original products and derivative products, and public domain and private domain, are not just copyright disputes in the metaverse scene, but also a difficult problem that the copyright system cannot reconcile. In this regard, this article draws on overseas experience, introduces the theory of copyright expansion, and combines the needs of China’s judicial practice and China’s national conditions, trying to solve the copyright legal issues in the metaverse scenario one by one through the “source of communication” theory, behavior restriction theory and interest game theory, and propose China’s response to the problems caused by the generalization of digital technology from the three dimensions of originality standard identification, object classification, and rights restriction rules.

Plausible Reasoning in an Algorithm for Generation of Good Classification Tests

The paper is devoted to the application of the plausible reasoning principles to symbolic machine learning. It seems for us that the applications are essential and necessary to improve the efficiency of ML algorithms. Many such algorithms produce and use rules in the form of implication. The generation of these rules with respect to the object classes is discussed. Our classification rules are specific. Their premise part, called good closed tests (GCTs), should cover as many objects as possible. One of the algorithms of GCTs generation called NIAGARA is presented. The algorithm is revisited and new procedures based on plausible reasoning are proposed. Their correctness is proved in propositions. We use the following rules: implication, interdiction, inductive rules of extending current sets of goal-oriented objects, rules of pruning the domain of searching solution. They allow to rise the effectiveness of algorithms.

A general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic

This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable artificial intelligence (XAI) refers to the transparency of AI systems in providing explanations for their predictions and decision-making processes, and contribute to improve prediction accuracy and enhance trust in AI systems. The focus of this paper relies on the interpretability challenges in ordinal classification problems within weather forecasting. Ordinal classification involves predicting weather phenomena with ordered classes, such as temperature ranges, wind speed, precipitation levels, and others. To address this challenge, a novel and general explicable forecasting framework, that combines inductive rules and fuzzy logic, is proposed in this work. Inductive rules, derived from historical weather data, provide a logical and interpretable basis for forecasting; while fuzzy logic handles the uncertainty and imprecision in the weather data. The system predicts a set of probabilities that the incoming sample belongs to each considered class. Moreover, it allows the expert decision-making process to be strengthened by relying on the transparency and physical explainability of the model, and not only on the output of a black-box algorithm. The proposed framework is evaluated using two real-world weather databases related to wind speed and low-visibility events due to fog. The results are compared to both ML classifiers and specific methods for ordinal classification problems, achieving very competitive results in terms of ordinal performance metrics while offering a higher level of explainability and transparency compared to existing approaches.