Decision Rules Research Articles

Assessing the Degree of Sustainability in Extractive Reserves in the Amazon Biome Using the Fuzzy Logic Tool for Decision Making

The Extractive Reserve (RESEX) was designed to protect rubber tapping communities and their livelihoods, thus guaranteeing environmental health. This study was carried out between 2021 and 2023 and aimed to propose a methodology based on the fuzzy logic method to assess the degree of sustainability in RESEXs in the state of Amazonas, Brazil. For this assessment, 10 indicators were used, represented through input variables in the fuzzy inference systems represented by the Environmental Subsystem (ES), Economic Subsystem (ECS), Social Subsystem (SS), and Institutional Subsystem (IS), with performances that converged so that the Sustainability System in the RESEX (SRE) system reached a performance value of 30.0, on a scale of 0 to 100, which translates into low sustainability in these spaces in the state of Amazonas. The methodology’s ability to represent the main phenomena that impact sustainability in the RESEX studied through linguistic variables and weight them in their complexities, as well as inferring a set of decision rules that reflect the knowledge of experts and which aim to quantitatively contextualise sustainability under uncertainty and imprecision in these areas, makes it a viable instrument to be applied and used by managers and decision-makers in the management of these spaces.

A Real-time Fuzzy Interacting Multiple-Model Velocity Obstacle Avoidance Approach for Unmanned Aerial Vehicles

This paper presents a new fuzzy interacting multiple-model velocity obstacle (FIMVO) approach for collision avoidance of unmanned aerial vehicles (UAVs). The proposed approach adopts in one framework the advantages of geometric collision avoidance approaches, namely of the velocity (VO), reciprocal velocity (RVO), and hybrid reciprocal velocity obstacle (HRVO) avoidance approaches combined with fuzzy logic. This leads to a combined decision-making rule, with real-time efficiency. The developed approach is compared with geometric conventional velocity obstacle avoidance approaches: VO, RVO, and HRVO avoidance approaches. The proposed approach is carefully evaluated and validated in a simulation environment and over real UAVs. The case study includes three mini UAVs and a human teleoperator who can control only one of them. The other UAVs used the computer-based teleoperator with the proposed and compared approaches. The performance criteria have been defined in four parts: trajectory smoothness, task performance, algorithm simplicity, and reliability. In 1000 independently repeated simulations, the performance results showed that the proposed FIMVO approach was 10 times better than the VO approach in terms of the number of avoided collisions. The statistical analysis demonstrates that the proposed FIMVO approach outperforms geometric velocity obstacle avoidance approaches concerning reliability and real-time efficiency.

Adaptive UAV Navigation Method Based on AHRS.

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy.

Bridging the feedback implementation gap: A comparison of empirical and rational decision rules in naturalistic psychotherapy

Objective: Previous research indicates positive effects of feedback based on rational or empirical decision rules in psychotherapy. The implementation of these usually session-to-session-based feedback systems into clinical practice, however, remains challenging. This study aims to evaluate decision rules based on routine outcome monitoring with reduced assessment frequency. Method: Data routinely collected every 5–20 sessions of N = 3758 patients treated with CBT in an outpatient clinic (M sessions = 42.8, SD = 15.4) were used to develop feedback decision rules based on the expected treatment response and nearest neighbors approach, the reliable change index, and method of percental improvement. The detection of patients at risk of treatment failure served as primary endpoint. Results: Significantly lower reliable improvement, higher reliable deterioration rates, and smaller effect sizes were found for patients identified at risk of treatment failure by all rules. The nearest neighbors-based approach showed the highest sensitivity regarding the detection of reliably deteriorated cases. Conclusion: Consistent with previous research, the empirical models outperformed the rational rules. Still, the first-time used percental improvement-based rule also showed satisfactory results. Overall, the results point to the potential of basic feedback systems that might be easier to implement in practice than session-to-session based systems.

The privacy preserving auction mechanisms in IoT-based trading market: A survey

Nowadays, the Internet of Things (IoT) technology has greatly promoted the transaction behavior. It has led to a shift from the traditional face-to-face, bartering business to today’s online virtual goods business, such as energy trading, spectrum, and so on. However, there exist two critical challenges in IoT-based trading market, which include “efficiency” and “safety”. “Efficiency” indicates a fair-trading decision-making rule, and “safety” indicates that the bidders’ privacy must be protected. To address the above challenges, numerous researchers are working on privacy-preserving auction mechanisms for IoT-based transactional markets, drawing insights from electronic auction theory and information security theory. However, a predominant focus on specific scenarios characterizes most existing works, lacking a comprehensive synthesis of the collective contributions to date. Therefore, this paper systematically elucidates privacy-preserving auction mechanisms for IoT-based transactional markets. Firstly, we expound the foundational knowledge of IoT-based auction markets, delving into auction theory and privacy theory through concise categorization. Secondly, we address both theoretical considerations (from the integration perspective of various privacy protection methods and auction mechanisms) and practical aspects (evaluating various real-world application scenarios). Each aspect undergoes meticulous scrutiny, providing practical assessments and prospects. Lastly, we propose future research directions. Key challenges include the absence of inference attack models for differential privacy, a dearth of algorithmic designs with privacy-preserving capabilities at the auction mechanism level, and the intricate balance between privacy and efficiency. Proposed solutions for future research directions include leveraging Bayesian inference and neural networks for effective attacks, designing autonomous privacy protection mechanisms, and addressing the privacy-efficiency trade-off through the application of Markov decision processes.

Innovative framework for effective service parts management in the automotive industry

Effective service parts management and demand forecasting are crucial for optimizing operations in the automotive industry. However, existing literature lacks a comprehensive framework tailored to the specific context of the Thai automotive sector. This study addresses this gap by proposing a strategic approach to service parts management and demand forecasting in the Thai automotive industry. Drawing on a diverse set of methodologies, including classical time series models and advanced machine learning techniques, various forecasting models were assessed to identify the most effective approach for predicting service parts demand. Categorization of service parts based on demand criteria was conducted, and decision rules were developed to guide stocking strategies, balancing the need to minimize service disruptions with cost optimization. This analysis reveals substantial cost savings potential through strategic stocking guided by the developed decision rules. Furthermore, evaluation of the performance of different forecasting models recommends the adoption of Support Vector Regressor (SVR) as the most accurate model for forecasting service parts demand in this context. This research contributes to the automotive service industry by providing a nuanced framework for service parts management and demand forecasting, leading to cost-effective operations and enhanced service quality. The findings offer valuable insights for practitioners and policymakers seeking to improve efficiency and sustainability in the Thai automotive sector.

Optimization of EWOC principle in BLRM design for phase 1 oncology trials

ABSTRACT Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs. This work discusses this design problem and proposes a practical solution to improve the performance of BLRM design. Specifically, we suggest increasing the EWOC cutoff from routine value 0.25 to a value between 0.3 and 0.4, which will increase the chance of finding the correct MTD with minimal compromise to overdosing risk. Our comparative simulation studies indicate that BLRM with an increased EWOC cutoff has comparable operating characteristics on the correct MTD selection and over-toxicity control as other dose escalation designs (BOIN, mTPI, keyboard, etc.). Moreover, we compare the methodology and operating characteristics of BLRM designs with various decision rules that allow more flexible overdosing control. A case study of dose escalation in a recent phase 1 oncology trial is provided to show how BLRM with optimal EWOC cutoff operates well in practice.

A Meta Algorithm for Interpretable Ensemble Learning: The League of Experts

Background. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains—medicine and finance—to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.

Target-oriented robust satisficing models for the single machine scheduling problems with release time

In this paper, we investigate single machine scheduling problems with release times and random processing times, where the release times can be either deterministic or random. The objective is to determine a scheduling sequence that exhibits strong out-of-sample performance. To achieve this, we employ target-oriented robust satisficing models to obtain the scheduling sequence. For the scheduling problem with deterministic release times, we derive an equivalent mixed-integer linear program and an approximate reformulation to address cases involving a large number of jobs. For the scheduling problem with random release times, we first demonstrate the challenges associated with providing an equivalent reformulation. To overcome this intractability, we propose an approximate reformulation based on the linear decision rule. Numerical experiments are conducted to demonstrate the superiority of our solutions through comparisons with several benchmarks. Furthermore, the numerical results demonstrate that a relatively higher target should be chosen if the worst-case performance is valued, otherwise, the decision-maker should set a relatively lower target to obtain better average performance.

Isotonic design for single-arm biomarker stratified trials.

In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not effective at all. We propose the isotonic stratified design for this problem. The design has a joint set of decision rules for biomarker positive and negative subjects and utilizes joint estimation of response probabilities using assumed monotonicity of response between the biomarker negative and positive subgroups. The new design reduces the sample size requirement when compared to running two Simon's designs in each biomarker positive and negative. For example, the new design requires 23%-35% fewer patients than running two Simon's designs for scenarios we considered. Alternatively, the new design allows evaluating the response probability in both biomarker negative and biomarker positive subgroups using only 40% more patients needed for running Simon's design in the biomarker positive subgroup only.

Perbandingan Akurasi Algoritma Naive Bayes dan Algoritma Decision Tree dalam Pengklasifikasian Penyakit Kanker Payudara

Cancer is one of the deadliest diseases in the world with a high increase in the number of cases every year Cancer disease with significant growth in cases, is a serious global challenge. The main focus of this research is breast cancer in Indonesia. Using a data mining approach, this study compares two main classification algorithms, namely Naive Bayes and Decision Tree, to identify breast cancer. Naive Bayes is a simple probabilistic approach, calculating probabilities assuming attribute independence. Decision Tree, as a popular algorithm, represents decision rules in the form of a tree. Through comparison with previous research on algorithms in other contexts, this study aims to find the algorithm with the highest accuracy in breast cancer classification. With the final result, the decision tree has a higher accuracy of 92.04% and naïve Bayes has an accuracy of 91.15%.This result proves that the decision tree is superior in the classification of breast cancer disease compared to naïve Bayes. The results of the study are expected to make an important contribution to the development of effective approaches for the diagnosis and treatment of breast cancer.

A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems

In the present study, we employed multiple decision tree algorithms to categorize cases and reflect the most efficient policies constructed by a reinforcement learning algorithm. These approaches treated a complex production, maintenance, and quality control optimization problem within a degrading manufacturing and remanufacturing system. The decision trees’ nodes represent the independent variables, while the trees’ leaves represent the set of function values. The reinforcement learning method revealed all optimization parameters and best policies, which were employed as the training sample for the tree algorithms. After constructing every decision tree, each resulting decision rule was used to solve the optimization problem, and its performance was assessed. Additionally, we performed a sensitivity analysis to determine if the pruning level impacts the objective function value and, generally, the effectiveness of the proposed approach.

Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model’s performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.

Performance of Febrile Infant Algorithms by Duration of Fever.

To analyze the performance of commonly used blood tests in febrile infants ≤90 days of age to identify patients at low risk for invasive bacterial infection (bacterial pathogen in blood or cerebrospinal fluid) by duration of fever. We conducted a secondary analysis of a prospective single-center registry that includes all consecutive infants ≤90 days of age with fever without a source evaluated at 1 pediatric emergency department between 2008 and 2021. We defined 3 groups based on caregiver-reported hours of fever (<2, 2-12, and ≥12) and analyzed the performance of the biomarkers and Pediatric Emergency Care Applied Research Network, American Academy of Pediatrics, and Step-by-Step clinical decision rules. We included 2411 infants; 76 (3.0%) were diagnosed with an invasive bacterial infection. The median duration of fever was 4 (interquartile range, 2-12) hours, with 633 (26.3%) patients with fever of <2 hours. The area under the curve was significantly lower in patients with <2 hours for absolute neutrophil count (0.562 vs 0.609 and 0.728) and C-reactive protein (0.568 vs 0.760 and 0.812), but not for procalcitonin (0.749 vs 0.780 and 0.773). Among well-appearing infants older than 21 days and negative urine dipstick with <2 hours of fever, procalcitonin ≥0.14 ng/mL showed a better sensitivity (100% with specificity 53.8%) than that of the combination of biomarkers of Step-by-Step (50.0% and 82.2%), and of the American Academy of Pediatrics and Pediatric Emergency Care Applied Research Network rules (83.3% and 58.3%), respectively. The performance of blood biomarkers, except for procalcitonin, in febrile young infants is lower in fever of very short duration, decreasing the accuracy of the clinical decision rules.

Active fault diagnosis for stochastic systems subject to non-convex input constraints

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of continuous-time stochastic systems subject to non-convex input constraints. The input is designed to minimize the probability of misdiagnosis. Since the misdiagnosis probability cannot be expressed in the closed form, its Chernoff upper bound likely tighter than its widely used Bhattacharyya upper bound is exploited as an alternative. The closed-form expression of the Chernoff distance under Gaussian distributions is correctly deduced. For calculation simplification and diagnosis performance enhancement purposes, a novel input design criterion is constructed for AFD based on the Chernoff distance. The optimization problem for AFD is formulated as a bilevel optimization, where the outer and inner layers optimize the Chernoff distance parameter and the input respectively. A convex relaxation is proposed to relax non-convex input constraints into convex ones, and the optimal solution to the relaxed inner input design problem is proven to be also optimal for the original inner problem whose objective function and input constraints are both non-convex under certain conditions, which is referred to as lossless relaxation. The relaxed inner input design problem under the constructed design criterion can be solved to global optimums, and the relaxed outer optimization is solved by a grid search method. Given the optimal input, faults are diagnosed based on the minimum misdiagnosis probability decision rule. The effectiveness of the proposed AFD approach is demonstrated by utilizing a planetary landing example.

ECG Feature Importance Rankings: Cardiologists vs. Algorithms.

Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. We found that the SHAP and LIME methods and Chi-squared test all worked well together with the native Random forest and Logistic regression feature rankings. Some methods gave inconsistent results, which included the Maximum Relevance Minimum Redundancy and Neighbourhood Component Analysis methods. The permutation-based methods generally performed quite poorly. A surprising result was found in the case of left bundle branch block, where T-wave morphology features were consistently identified as being important for diagnosis, but are not used by clinicians.

Halakhic Crypticity

Abstract Medieval Jewish philosophers used cryptic writing 1) to protect innocent believers whose faith could be harmed by uncustomary ideas; 2) to protect the philosopher, whose societal standing might be risked through the expression of uncustomary views; and 3) as a normative characteristic of how philosophy was written. This article demonstrates that, in the halakhic literature, this same technique was utilized by halakhic decisors, and for reasons similar to those of philosophers. A contemporary example helps to make the point. Rabbi Shlomo Zalman Auerbach, one of the most prominent twentieth century rabbis, used this approach for two of the three reasons cited: to protect readers from falling into inappropriate religious practice and to protect his own status in a community that valued halakhic uniformity. Rabbi Auerbach thus exemplifies the use of halakhic crypticity to maintain a distinction between theoretical “decisions rules” and practical “conduct rules.”

ANALYSIS OF MEASUREMENT ACCURACY DURING VERIFICATION OF MIT TEMPERATURE METERS

The paper presents an algorithm and the results of calculating the confidence limits of the measurement error of resistance (voltage) during verification of multichannel precision temperature meters MIT 2 and MIT 8. It is established that the actual error value of the meter in operation can exceed the permissible value by 4.3 times. In the temperature equivalent, the additional error can reach 130% of the limit of the basic permissible error of the reference platinum resistance thermometer PTS-10M of the first category. The necessity of attributing the measurement results by MIT meters to the most responsible measurements, for which even the minimum excess of the permissible error is not allowed, is justified. The corresponding decision-making rule has been formulated. The minimum ratio of errors of the reference and verifiable measuring instruments has been determined, in which the probability of erroneous recognition of an obviously suitable meter as unsuitable does not exceed 0.3 – the limit set in MI 186-88 "GSI. Measuring instruments. Setting the parameters of the verification methods" values. The required error ratio is no worse than 1/2. For widely used meters MIT 2.05, MIT 8.10, MIT 8.15, methods are proposed to improve the accuracy of measurements and ensure the required reliability of verification using recommended standards. To verify the relatively new and most accurate meters MIT 8.20, MIT 8.30, the need to use a secondary resistance standard is justified. The work can be useful to specialists involved in the development, operation of temperature measuring instruments and their metrological support.

Prospective diagnostic accuracy study of history taking and physical examination for adults with vertigo in general practice: study protocol

IntroductionVertigo is a prevalent and burdensome symptom. More than 80% of patients with vertigo are primarily treated by their general practitioner (GP) and are never referred to a medical specialist....

Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent

Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood; thus, they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces RELAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task. We then find the optimal CFs via deep reinforcement learning (DRL) with discretecontinuous hybrid action space. In addition, we develop a distillation algorithm to extract decision rules from the DRL agent's policy in the form of a decision tree to make the process of generating CFs itself interpretable. Extensive experiments conducted on six tabular datasets have shown that RELAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, we show the ability of our method to provide actionable recommendations and distill interpretable policy explanations in two practical, real-world use cases.