Classification Rules Research Articles

Conditioning w.r.t. random sets – Part 2: combination rules

Part 2 of this article is devoted to the analysis of a new class of combination rules on belief functions generated by conditioning w.r.t. random sets and their linear convex combinations. Each such rule obeys two requirements: the consensus requirement with the conjunction rule means that this rule coincides with Dempster's rule on non-conflicting sources of information, and the consensus requirement with a mixture rule means that the result must be a specialization of their mixture combination. We show that this new class of combination rules is closed under convex combinations of such rules and their compositions. This allows us to implement the conflict management using the introduced functionals for measuring the specialization relation on belief functions. We also introduce similar rules for idempotent combination rules on credal sets obeying the similar consensus requirements and study the results obtained using various examples.

A multi-verse Optimizer Approach Based on a Salp Swarm for Image Feature Selection to Observe Changes in Urban Lands

Many changes have occurred in most urban areas, and it is usually governed by many geographical and social factors, the result will be either the expansion or contraction of these cities. In this paper, an optimizer method called salp swarm optimization (SSO) is used to build a driving force model for urban lands according to the changes in the driving force mechanism. The principle of (SSO) is extracting the driving force classification rules corresponding to different types of land use change samples by imitating the behavior of salp. The classification rules are constructed in the form of "IF ... THEN", and three different fitness functions are selected for simulation verification. The data set used in this search represents images from Global Positioning System (GPS) satellites and remote sensing data. The experimental results show that the overall accuracy evaluation of the salp swarm optimization model is superior to other algorithms, indicating that the SSA is feasible for land use change modeling.

Adaptive fuzzy neighborhood decision tree

Decision tree algorithms have gained widespread acceptance in machine learning, with the central challenge lying in devising an optimal splitting strategy for node sample subspaces. In the context of continuous data, conventional approaches typically involve fuzzifying data or adopting a dichotomous scheme akin to the CART tree. Nevertheless, fuzzifying continuous features often entails information loss, whereas the dichotomous approach can generate an excessive number of classification rules, potentially leading to overfitting. To address these limitations, this study introduces an adaptive growth decision tree framework, termed the fuzzy neighborhood decision tree (FNDT). Initially, we establish a fuzzy neighborhood decision model by leveraging the concept of fuzzy inclusion degree. Furthermore, we delve into the topological structure of misclassified samples under the proposed decision model, providing a theoretical foundation for the construction of FNDT. Subsequently, we utilize conditional information entropy to sift through original features, prioritizing those that offer the maximum information gain for decision tree nodes. By leveraging the conditional decision partitions derived from the fuzzy neighborhood decision model, we achieve an adaptive splitting method for optimal features, culminating in an adaptive growth decision tree algorithm that relies solely on the inherent structure of real-valued data. Experimental evaluations reveal that, compared with advanced decision tree algorithms, FNDT exhibits a simple tree structure, stronger generalization capabilities, and superior performance in classifying continuous data.

Strategi Pengelolaan Kelas yang Efektif dalam Meningkatkan Disiplin Belajar Siswa

Education data according to sources from Databoks states that the Indonesian population with a high school education of 15 years or more reaches 30.22%, while junior high school reaches 22.74%, and elementary school reaches 24.62%. It is very unfortunate that with a very large population, the majority of Indonesians have very little education. Of 12 countries in Asia, Indonesia's education quality is in 12th place, with Vietnam at the bottom, according to a survey conducted by the Political and Economic Risk Consultant (PERC). Data collected by the Swedish World Economic Forum in 2000 showed that Indonesia only ranked 37th out of 57 countries surveyed. In addition, an education quality survey conducted by the same organization in Indonesia shows that Indonesia is only considered a follower rather than a technology leader in 53 countries in the world. This research uses a qualitative descriptive research method by emphasizing literature study or literature study in analyzing variables that are relevant to the discussion in the research. The results of this research show that the low quality of education in Indonesia is caused by the learning process, especially in classroom management. Basically, classroom management is very important so that students can understand learning comfortably because of supportive classroom conditions. There are several strategies obtained in this research, namely planning and implementing class rules, using inclusive teaching techniques and approaches, and developing positive relationships and emotional support for students.

CON-FOLD Explainable Machine Learning with Confidence

Abstract FOLD-RM is an explainable machine learning classification algorithm that uses training data to create a set of classification rules. In this paper, we introduce CON-FOLD which extends FOLD-RM in several ways. CON-FOLD assigns probability-based confidence scores to rules learned for a classification task. This allows users to know how confident they should be in a prediction made by the model. We present a confidence-based pruning algorithm that uses the unique structure of FOLD-RM rules to efficiently prune rules and prevent overfitting. Furthermore, CON-FOLD enables the user to provide preexisting knowledge in the form of logic program rules that are either (fixed) background knowledge or (modifiable) initial rule candidates. The paper describes our method in detail and reports on practical experiments. We demonstrate the performance of the algorithm on benchmark datasets from the UCI Machine Learning Repository. For that, we introduce a new metric, Inverse Brier Score, to evaluate the accuracy of the produced confidence scores. Finally, we apply this extension to a real-world example that requires explainability: marking of student responses to a short answer question from the Australian Physics Olympiad.

Efficient and Fair Healthcare Rationing

The rationing of healthcare resources has emerged as an important issue, which has been discussed by medical experts, policy-makers, and the general public. We consider a rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories, and each category has a priority ranking over the patients. We present a class of allocation rules that respect the priorities, comply with the eligibility requirements, allocate the largest feasible number of units, and do not penalize agents for rising in the priority ranking of a category. The rules characterize all possible allocations that satisfy the first three properties and are polynomial-time computable.

Analysis of Thermodynamic Models for Liquid-Vapor Equilibrium: Evaluating Accuracy and Applicability

This article investigates the liquid-vapor equilibrium of four binary refrigerant systems: R134a + R290, R152a + R1234ze, R152a + R1243zf, and R1243zf + R134a. The study employs three thermodynamic models for accurate predictions: the Peng-Robinson equation with the classical mixture rule of van der Waals (vdW) and the Wilson model, the PC-SAFT equation, and the PR-MC-WS-NRTL model. Activity coefficients are determined using the Peng-Robinson equation with vdW and the Wilson model. The PC-SAFT equation and the PR-MC-WS-NRTL model are also applied to model the data. The calculated results show good agreement with reference data. Favorable agreements exist between the calculated results and the reference data, with relative errors remaining below (0.15 and 0.42) % for the molar fraction and the pressure, respectively. This research provides valuable insights into the accuracy and applicability of different thermodynamic models in predicting liquid-vapor equilibrium within refrigerant systems.

Modified Rules for Classification of Variants Associated With Disorders of Somatic Mosaicism.

Disorders of somatic mosaicism (DoSMs) are rare genetic disorders arising from postzygotic alteration leading to segmental/nonsegmental disease. Current professional guidelines for standardized variant interpretation focus on germline and cancer variants, making them suboptimal for DoSM variant interpretation. The Brain Malformations Variant Curation Expert Panel (BMVCEP) modified existing guidelines to account for brain-specific disorders of somatic mosaicism, but applicability to other DoSM presentations is limited. At Washington University in St. Louis School of Medicine, we have adopted the BMVCEP interpretation framework but suggested alterations that make it more suitable for generalized DoSM variant classification. These modifications include (1) expanding applicability beyond genes associated with brain malformations, (2) introduction of a criterion to interpret truncating variants at the C-terminus of gain of function genes, (3) establishment of a variant allele fraction (VAF) cutoff for applying de novo criteria, and (4) demonstration that in silico prediction tools are relevant to interpretation of gain of function missense variants. Furthermore, modifications to BMVCEP guidelines reduce the number of variants classified as uncertain. The variant classification considerations that we propose have the potential to improve the accuracy of somatic variant classification, better inform clinical care, and may benefit clinical laboratories also conducting DoSM testing.

Handling the Imbalanced Problem in Agri-Food Data Analysis.

Imbalanced data situations exist in most fields of endeavor. The problem has been identified as a major bottleneck in machine learning/data mining and is becoming a serious issue of concern in food processing applications. Inappropriate analysis of agricultural and food processing data was identified as limiting the robustness of predictive models built from agri-food applications. As a result of rare cases occurring infrequently, classification rules that detect small groups are scarce, so samples belonging to small classes are largely misclassified. Most existing machine learning algorithms including the K-means, decision trees, and support vector machines (SVMs) are not optimal in handling imbalanced data. Consequently, models developed from the analysis of such data are very prone to rejection and non-adoptability in real industrial and commercial settings. This paper showcases the reality of the imbalanced data problem in agri-food applications and therefore proposes some state-of-the-art artificial intelligence algorithm approaches for handling the problem using methods including data resampling, one-class learning, ensemble methods, feature selection, and deep learning techniques. This paper further evaluates existing and newer metrics that are well suited for handling imbalanced data. Rightly analyzing imbalanced data from food processing application research works will improve the accuracy of results and model developments. This will consequently enhance the acceptability and adoptability of innovations/inventions.

Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

Recently, there has been a huge increase in the different ways to manufacture polymer-based sensors. Methods like additive manufacturing, microfluidic preparation, and brush painting are just a few examples of new approaches designed to improve sensor features like self-healing, higher sensitivity, reduced drift over time, and lower hysteresis. That being said, we believe there is still a lot of potential to boost the performance of current sensors by applying modeling, classification, and machine learning techniques. With this approach, final sensor users may benefit from inexpensive computational methods instead of dealing with the already mentioned manufacturing routes. In this study, a total of 96 specimens of two commercial brands of Force Sensing Resistors (FSRs) were characterized under the error metrics of drift and hysteresis; the characterization was performed at multiple input voltages in a tailored test bench. It was found that the output voltage at null force (Vo_null) of a given specimen is inversely correlated with its drift error, and, consequently, it is possible to predict the sensor’s performance by performing inexpensive electrical measurements on the sensor before deploying it to the final application. Hysteresis error was also studied in regard to Vo_null readings; nonetheless, a relationship between Vo_null and hysteresis was not found. However, a classification rule base on k-means clustering method was implemented; the clustering allowed us to distinguish in advance between sensors with high and low hysteresis by relying solely on Vo_null readings; the method was successfully implemented on Peratech SP200 sensors, but it could be applied to Interlink FSR402 sensors. With the aim of providing a comprehensive insight of the experimental data, the theoretical foundations of FSRs are also presented and correlated with the introduced modeling/classification techniques.

Quantum Cellular Automata for Quantum Error Correction and Density Classification.

Quantum cellular automata are alternative quantum-computing paradigms to quantum Turing machines and quantum circuits. Their working mechanisms are inherently automated, therefore measurement free, and they act in a translation invariant manner on all cells or qudits of a register, generating a global rule that updates cell states locally, i.e., based solely on the states of their neighbors. Although desirable features in many applications, it is generally not clear to which extent these fully automated discrete-time local updates can generate and sustain long-range order in the (noisy) systems they act upon. In particular, whether and how quantum cellular automata can perform quantum error correction remain open questions. We close this conceptual gap by proposing quantum cellular automata with quantum-error-correction capabilities. We design and investigate two (quasi)one dimensional quantum cellular automata based on known classical cellular-automata rules with density-classification capabilities, namely the local majority voting and the two-line voting. We investigate the performances of those quantum cellular automata as quantum-memory components by simulating the number of update steps required for the logical information they act upon to be afflicted by a logical bit flip. The proposed designs pave a way to further explore the potential of new types of quantum cellular automata with built-in quantum-error-correction capabilities.

Applicability of ISNT rule to retinal nerve fiber layer thickness by SD-OCT in normal subjects.

The usefulness of the Inferior>Superior>Nasal>Temporal (ISNT) rule for the neuroretinal rim (NRR) has been widely used in differentiating normal eyes from glaucoma, but only a few studies have assessed whether this classical ISNT rule applies to the retinal nerve fibre layer (RNFL). This study aimed to determine the applicability of the ISNT rule for the peripapillary RNFL thickness in normal eyes using Spectral Domain Optical Coherence Tomography (SD-OCT) and assess if variants of the ISNT rule apply. A cross-sectional study was conducted on 120 eyes of 62 healthy subjects who fulfilled the study criteria. Peripapillary RNFL thickness was measured by OCT scan and each eye's global, superior, temporal, nasal, and inferior quadrant thickness was noted. The values obtained were analyzed to determine the percentage of eyes obeying the ISNT rule and its variants. The ISNT rule for RNFL thickness was applicable for normal subjects in only 53.33% of cases. Removing the nasal quadrant from analysis increased the number of eyes obeying the IST rule to 71.66%. Further exclusion of the temporal quadrant showed almost the same results (75%) for the IS rule. The ISNT rule for RNFL thickness could be validated in only 53.33% of normal individuals. Though documented as useful for NRR during ophthalmoscopy in glaucoma diagnosis, the ISNT rule did not apply to the quadrant values on RNFL on SD-OCT examination.

High-Precision Heterogeneous Satellite Image Manipulation Localization: Feature Point Rules and Semantic Similarity Measurement

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.

‘Chairs of Conflict Resolution’: training young children to resolve conflicts in Greek kindergartens

ABSTRACT The Chairs of Conflict Resolution (CoCR) is a process developed for training children to discuss their conflicts constructively so they experience win-win outcomes. The present study examines the discussion taking place during the use of CoCR with young children 4–6-years-old. The discussion between the two children proceeded in four phases: (a) Describing the conflict where the ‘protestor’ blamed the other child and the ‘reactor’ accepts or denies his/her actions; (b) Discussing emotions; (c) Elaboration where they explain further their actions and intentions, evaluate their behaviour, recall class rules or CoCR rules, propose alternative ways of actions and (d) Resolution where children apologize and sometimes hug. Through teacher’s scaffolding children were able to resolve their conflicts in a mutually beneficial way. The outcomes are discussed in relation to the development of perspective-taking and social understanding and recommendations are being made for its successful implementation in early year settings.

Improving rule-based classifiers by Bayes point aggregation

The widespread adoption of artificial intelligence systems with continuously higher capabilities is causing ethical concerns. The lack of transparency, particularly for state-of-the-art models such as deep neural networks, hinders the applicability of such black-box methods in many domains, like the medical or the financial ones, where model transparency is a mandatory requirement, and hence white-box models are largely preferred over potentially more accurate but opaque techniques.For this reason, in this paper, we focus on ruleset learning, arguably the most interpretable class of learning techniques. Specifically, we propose Bayes Point Rule Classifier, an ensemble methodology inspired by the Bayes Point Machine, to improve the performance and robustness of rule-based classifiers. In addition, to improve interpretability, we propose a technique to retain the most relevant rules based on their importance, thus increasing the transparency of the ensemble, making it easier to understand its decision-making process.We also propose FIND-RS, a greedy ruleset learning algorithm that, under mild conditions, guarantees to learn hypothesis with perfect accuracy on the training set while preserving a good generalization capability to unseen data points.We performed extensive experimentation showing that FIND-RS achieves state-of-the-art classification performance at the cost of a slight increase in the ruleset complexity w.r.t. the competitors. However, when paired with the Bayes Point Rule Classifier, FIND-RS outperforms all the considered baselines.

Supervised, Unsupervised Learning Methods in Data Mining: A Comprehensive Survey

Abstract: Data mining is the area of computational science consider as the subset of Artificial Intelligence, Machine Learning that concentrate on cognizing patterns and structures in data to promote learning, analyzing, and decision making beyond human interaction. In simplest term, data mining allows the user to feed data mining algorithms on an immense amount of data and then make the computer to make data-driven recommendations and decisions based on the input or training data. Data is the essential component of all business. Data mining with supervised and unsupervised methods is the key to unlock the value of business data and keep ahead of the competition. This paper presents a comprehensive survey on Data mining process, supervised, unsupervised methods, through a survey of literature considering the significant research areas using classification, clustering, and association rule mining.

An Electrolyte‐Gated InGaZnO Phototransistor that Emulates Visual Experience‐Dependent Plasticity

AbstractInspired by neurobiological learning rules, bionic devices that simulate the fundamental functions of synapses and neurons provide a highly effective approach to neuromorphic computing. Among various learning rules, the Bienenstock‐Cooper‐Munro (BCM) learning rule can explain the threshold sliding effect of synaptic weight modification in the visual cortex, which is difficult to explain with the classical Hebb's rule. Existing research mainly focuses on exploiting electrical stimulation to implement the BCM rule, while the optical implementation is still unexplored. In this paper, the light‐history‐dependent BCM learning rule is implemented with electrolyte‐gated InGaZnO (IGZO) transistors. The channel conductance can be modulated through light illumination and electrical stimulation. By utilizing the light‐history‐dependent property of the IGZO electrolyte‐gated transistor and following the triplet‐spike‐timing‐dependent plasticity (STDP) rules, the BCM learning rule is successfully emulated in a single device. Moreover, the light‐history‐dependent property enables a variety of bionic vision functions including image edge detection and associative memory. This work provides a paradigm for the novel implementation of the BCM rule and paves the way for further development of machine vision systems.

Lower and upper bounds for scheduling a real-life assembly problem with precedences and resource constraints

In this work, we consider a real-life scheduling problem involving the production of off-road vehicles using a three-level assembly process, subject to precedence, machines, and resource constraints. This problem shares multiple characteristics with other scheduling problems such as the Parallel Machine Scheduling and Flexible Flow Shop Problems. However, it also comprises less investigated aspects such as a specific job precedence structure resulting in a directed rooted in-tree and a global resource constraint limiting the number of simultaneously active machines. We present a straightforward adaptation of a time-indexed mathematical formulation to the problem and introduce a new lower-bounding procedure. To solve the problem, we propose constructive heuristics based on classical priority rules and adaptations of job sequencing heuristics. We also propose CORE, a specialized approach tailored to leverage the problem structure. All the algorithms are extensively evaluated on two benchmark sets, various shop floor configurations, and eight real-life scenarios. Our results reveal the general effectiveness of job sequencing approaches and demonstrate the overall superiority of CORE.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.

Belief rule learning and reasoning for classification based on fuzzy belief decision tree

The belief rules which extend the classical fuzzy IF-THEN rules with belief consequent parts have been widely used for classifier design due to their capabilities of building linguistic models interpretable to users and addressing various types of uncertainty. However, in the rule learning process, a high number of features generally results in a belief rule base with large size, which degrades both the classification accuracy and the model interpretability. Motivated by this challenge, the decision tree building technique which implements feature selection and model construction jointly is introduced in this paper to learn a compact and accurate belief rule base. To this end, a new fuzzy belief decision tree (FBDT) with fuzzy feature partitions and belief leaf nodes is designed: a fuzzy information gain ratio is first defined as the feature selection criterion for node fuzzy splitting and then the belief distributions are introduced to the leaf nodes to characterize the class uncertainty. Based on the initial rules extracted from the constructed FBDT, a joint optimization objective considering both classification accuracy and model interpretability is then designed to further reduce the rule redundancy. Experimental results based on real datasets show that the proposed FBDT-based classification method has much smaller rule base and better interpretability than other rule-based methods on the premise of competitive accuracy.