Generate Decision Rules Research Articles

Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest

To address the limitation of traditional machine learning models in explaining the rockburst intensity prediction process, this study proposes an interpretable rockburst intensity prediction model. The model was developed using 350 sets of actual rockburst sample data to explore the impact of input metrics on the final rockburst intensity level. The collected data underwent pre-processing using the isolation forest algorithm and synthetic minority oversampling technique. The random forest model was optimized through 5-fold cross-validation and the Optuna framework, resulting in the establishment of an Optuna-random forest (Op-RF) model that generates decision rules through its internal decision tree, utilizing the properties of the random forest model. The model was further interpreted using the Shapley additive explanations algorithm, both locally and globally. The results demonstrate that the proposed model achieved an area under curve score of 0.984. In comparison to eight other machine learning models, the proposed Op-RF model demonstrated superior accuracy, precision, recall, and F1 score. The model provides a transparent explanation of the prediction process, linking impact characteristics to the final output. Additionally, a cloud deployment method for the rockburst intensity prediction model is provided and its effectiveness is demonstrated through engineering verification. The proposed model offers a new approach to the application of machine learning in rockburst intensity prediction.

New methods for statistical decision making in conditions of a limited volume of observations and with a prioriy parametric uncertainty

A new generalized adaptive algorithm for learning to make statistical decisions for exponential families of distributions with a priori parametric uncertainty in conditions of small samples has been developed. A generalized decision rule is presented, obtained by estimating unknown parameters of distributions, as well as a decision rule that satisfies the necessary optimality conditions: constancy of the average probability of a type I error and unbiasedness. Specific decision procedures for partial distributions obtained from a generalized algorithm are considered. Numerical examples are given. The effectiveness of the developed optimal procedure for small samples is shown.

Concept cognition for knowledge graphs: Mining multi-granularity decision rule

As part of cognitive intelligence, concept cognition for knowledge graphs aims to clearly grasp the typical characteristics of the things referred to by the concept, which can provide prior knowledge for machine understanding and thinking. Different from concept learning and formal concept analysis that learn new concepts from data and the general decision rule that comes from an independent decision table, this paper cognizes an existing concept by decision rules that come from multiple granularities. Specifically, 1) concept cognition for knowledge graphs is realized from the perspective of mining multi-granularity decision rule. 2) Decision tables corresponding to four granularities form a multi-granularity decision table group, and then the result from coarser granularity can guide and help obtaining the result from finer granularity. 3) We propose a framework for mining multi-granularity decision rules, which involves going from a multi-granularity decision table group to the frequent maximal attribute patterns to the decision rules to the credible decision rules. Finally, we verified effectiveness of dividing positive and negative data, monotonicity of attribute patterns in a multi-granularity decision table group, and downward monotonicity of credibility, and observed the impact of the parameter min_cov and min_conf on execution times.

A hybridized red deer and rough set clinical information retrieval system for hepatitis B diagnosis

Healthcare is a big concern in the current booming population. Many approaches for improving health are imposed, such as early disease identification, treatment, and prevention. Therefore, knowledge acquisition is highly essential at different stages of decision-making. Inferring knowledge from the information system, which necessitates multiple steps for extracting useful information, is one technique to address this problem. Handling uncertainty throughout data analysis is also another challenging task. Computer intelligence is a step forward to this end while selecting characteristics, classification, clustering, and developing clinical information retrieval systems. According to recent studies, swarm optimization is a useful technique for discovering key features while resolving real-world issues. However, it is ineffective in managing uncertainty. Conversely, a rough set helps a decision system generate decision rules. This produces decision rules without any additional information. In order to assess real-world information systems while managing uncertainties, a hybrid strategy that combines a rough set and red deer algorithm is presented in this research. In the red deer optimization algorithm, the suggested method selects the optimal characteristics in terms of the degree of dependence on the rough set. In order to determine the decision rules, further a rough set is used. The efficiency of the suggested model is also contrasted with that of the decision tree algorithm and the conventional rough set. An empirical study on hepatitis disease illustrates the viability of the proposed research as compared to the decision tree and crisp rough set. The proposed hybridization of rough set and red deer algorithm achieves an accuracy of 91.7% accuracy. The acquired accuracy for the decision tree, and rough set methods is 82.9%, and 88.9%, respectively. It suggests that the proposed research is viable.

Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study

This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employs two tree-based machine learning classifiers and a deterministic point forecaster; a statistical regression model. Hyperparameters for the tree-based classifiers are optimized for statistical performance based on recall, precision, and F1-score. The deterministic forecaster is adapted from the literature on electricity price forecasting for the classification task. Additionally, one tree-based model prioritizes interpretability, generating decision rules that are subsequently utilized to produce price spike forecasts. For all models, we evaluate the final statistical and economic predictive performance. The interpretable model is analyzed for the trade-off between performance and interpretability. Numerical results highlight the significance of complementing statistical performance with economic assessment in electricity price spike forecasting. All experiments utilize data from Alberta’s electricity market.

Path Planning Algorithm and ID3 Decision Tree Model Application of Scenic Intelligent Navigation System

The assumption space of ID3 algorithm contains all decision trees, and the search space is also a complete assumption space. The current assumptions based on the information gain standard, and uses the information gain to reduce the sensitivity to individual training sample errors. Based on ID3 decision tree algorithm, this paper provides new ideas and methods for the quality optimization of tourist attractions. Firstly, ID3 decision tree algorithm model is constructed based on information entropy theory; Then, based on the literature research and system research methods, the quality evaluation indicator system of tourist attractions is constructed; Finally, based on the mathematical model and evaluation indicator system, the empirical research was carried out, and the data of 17 tourist attractions were collected. The expected research results were obtained according to the data preprocessing, data calculation process, decision tree construction and rule generation process. The research finds that the quality evaluation process of tourist attractions based on ID3 decision tree model, through data preprocessing, data calculation, decision tree construction and decision rules generation, etc. it can be concluded that the "Tourism environment quality" attribute is the most important in this evaluation system, and "Tourism landscape quality" is relatively important. Tourist attractions can improve the work and quality of tourist attractions according to the research results.

On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme.

Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure-activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure-activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question. Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted. Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: ; non-active: ). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning

Biochar is a high carbon content organic compound has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant based, animal based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning based data analysis is necessary to find the relationship between biochar production parameters as well as feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The condition attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, oxygen) and pyrolysis conditions (operating temperature, heating rate residence time) while the decision attributes considered were yield, carbon content, and higher heating value. The rules generated were tested against a set of validation data and evaluated for its scientific coherency. Based on then decision rules generated, biomass with ash content of 11 to 14 wt%, volatile matter of 60 to 62 wt% and carbon content of 42 to 45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via pyrolysis process at operating temperature of 425°C to 475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

An Approach to Solve Some Unsolved Limitations: Rough Set Theory

Rough set theory is a mathematical approach to dealing with uncertainty and vagueness in data. It was introduced as a way to approximate classical sets using lower and upper bounds. Rough set theory has been applied to various domains such as data mining, knowledge discovery, machine learning, soft computing, medical analysis, synthesis of switching circuits, and civil engineering. Rough set theory can handle imprecise and noisy data by finding structural relationships and dependencies among attributes. It can also reduce redundant and irrelevant attributes and generate decision rules from data. Rough set theory is closely related to fuzzy set theory, but differs in that it uses multiple memberships instead of partial memberships to model uncertainty. Lot of research works have taken place in this domain with many fruitful outcomes that helped lot in expanding this field to much wider reach of knowledge irrespective of the domain concerned to mathematical and technological application development. In lieu of such research-oriented progress in different versatile domain, many areas of research has remained untouched as many a problem remained unsolved being shrouded under the mystery. This paper deals mainly with some of the still unsolved questions on Rough Set that are under study for new discovery and a way to overcome such limitations citing proper techniques, examples, working models, and graphs.

Development of knowledge-based clinical decision support system for the management of cardiovascular diseases

Abstract Background Knowledge-based clinical decision support systems (CDSS) are technological tools that analyze patient information and present justified diagnostic and therapeutic suggestions to the user. They improve adherence to evidence-based medicine and reduce medical errors. Material and methods We have developed an advanced CDSS with the following components: 1. Knowledge Management System, which allows the introduction of interoperable clinical variables organized in taxonomies, and the generation, validation and direct maintenance of decision rules by experts doctors. 2. Smart Assistant for Medical Reports Generation, which facilitates user interaction for the introduction of clinical data (natural language and/or data entry forms) and decission making supported by real-time suggestions that are automatically generated by 3. Inference Engine. Rules and variables derived from the most relevant cardiovascular Clinical Practice Guidelines (CPGs) are introduced by cardiologists into the system. The rules undergo a process of peer review and internal and external validation with anonymized clinical cases. Results More than 2,000 clinical variables and more than 5,000 rules corresponding to more than 80 CPGs and consensuns papers have been created and validated. An internal and external pilot validation of the system has been successfully carried out for the main cardiovascular problems, such as heart failure, atrial fibrillation, valvulopathies, coronary syndromes, cardiomyopathies, hypertension, diabetes and dyslipidaemias, using more than 400 cases. Conclusion Our CDSS is capable of providing real-time diagnostic and therapeutic recommendations for complex scenarios in cardiovascular disease, combining thousands of variables and decision rules in a single system. Prospective evaluation of the system in real clinical environments, certification and integration in Electronic Medical Records for clinical use is required. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): DILEMMA solutions, SL

Decision Rule Prediction for Assessment of Rotor Spun Cotton Yarn Strength Using Rough Set

ABSTRACT Being an agro-based natural product, cotton always poses vagueness in the parameters. Hence, formulation of decision rules for prediction of cotton yarn quality from the imprecise cotton fiber properties is an intricate problem. Yarn tenacity is considered an imperative quality parameter in the cotton textile industry. Modelling of yarn tenacity has remained as the cynosure of research for textile engineers. In recent years, rough set theory has evolved as one of the most important techniques used for handling imprecise data. One of the cardinal uses of rough set theory is its application to rule generation. Our approach focuses on the elimination of the redundant data set in order to generate effective decision rules which retain the accuracy of the original data set. In this work rough set theory is employed to generate decision rules to predict yarn tenacity from six input parameters. The validation results prove that the generated 45 decision rules accurately predicted 14 out of 16 unknown test data. Thus, in the present competitive market, this model is potent for getting recognition from the textile industry.

A domain knowledge enhanced yield based deep learning classifier identifies perineural invasion in oral cavity squamous cell carcinoma.

Perineural invasion (PNI), a form of local invasion defined as the ability of cancer cells to invade in, around, and through nerves, has a negative prognostic impact in oral cavity squamous cell carcinoma (OCSCC). Unfortunately, the diagnosis of PNI suffers from a significant degree of intra- and interobserver variability. The aim of this pilot study was to develop a deep learning-based human-enhanced tool, termed domain knowledge enhanced yield (Domain-KEY) algorithm, for identifying PNI in digital slides. Hematoxylin and eosin (H&E)-stained whole-slide images (WSIs, n = 85) were obtained from 80 patients with OCSCC. The model structure consisted of two parts to simulate human decision-making skills in diagnostic pathology. To this aim, two semantic segmentation models were constructed (i.e., identification of nerve fibers followed by the diagnosis of PNI). The inferred results were subsequently subjected to post-processing of generated decision rules for diagnostic labeling. Ten H&E-stained WSIs not previously used in the study were read and labeled by the Domain-KEY algorithm. Thereafter, labeling correctness was visually inspected by two independent pathologists. The Domain-KEY algorithm was found to outperform the ResnetV2_50 classifier for the detection of PNI (diagnostic accuracy: 89.01% and 61.94%, respectively). On analyzing WSIs, the algorithm achieved a mean diagnostic accuracy as high as 97.50% versus traditional pathology. The observed accuracy in a validation dataset of 25 WSIs obtained from seven patients with oropharyngeal (cancer of the tongue base, n = 1; tonsil cancer, n = 1; soft palate cancer, n = 1) and hypopharyngeal (cancer of posterior wall, n = 2; pyriform sinus cancer, n = 2) malignancies was 96%. Notably, the algorithm was successfully applied in the analysis of WSIs to shorten the time required to reach a diagnosis. The addition of the hybrid intelligence model decreased the mean time required to reach a diagnosis by 15.0% and 23.7% for the first and second pathologists, respectively. On analyzing digital slides, the tool was effective in supporting human diagnostic thinking. The Domain-KEY algorithm successfully mimicked human decision-making skills and supported expert pathologists in the routine diagnosis of PNI.

A rough set approach for classification of fabric defects

PurposeIn recent years, rough set theory has evolved as one of the most promising classification techniques. One of the cardinal uses of rough set theory is its application for rule generation. The purpose of this paper is to propose a real-time fabric inspection technique. This work deals with the multi-class classification of fabric defects using rough set theory.Design/methodology/approachThis technique focuses on the classification of fabric defects using the effective decision rules envisaged by rough set theory. In the proposed work, the six features of 50 images have been used for multiclass classification of fabric defects.FindingsIn this work, 40 images were used for generation of decision rules and 10 unseen images were used for validation out of which nine images are accurately predicted by the proposed technique.Originality/valueThe proposed method accurately identified 9 out of 10 testing defects. The obtained decision rules provide an insight about the classification method which ensures that the prediction accuracy can be improved further by framing more robust decision rules with the help of a large training data set. Thus, with the support of modern computational systems this method is potent in getting recognition from the textile industry as a real-time classification technique.

Development of knowledge-based clinical decision support system for the management of cardiovascular diseases

Abstract Background Knowledge-based clinical decision support systems (CDSS) are technological tools that analyze patient information and present justified diagnostic and therapeutic suggestions to the user. They improve adherence to evidence-based medicine and reduce medical errors. Material and methods We have developed an advanced CDSS with the following components: 1. Knowledge Management System, which allows the introduction of interoperable clinical variables organized in taxonomies, and the generation, validation and direct maintenance of decision rules by experts doctors. 2. Smart Assistant for Medical Reports Generation, which facilitates user interaction for the introduction of clinical data (natural language and/or data entry forms) and decission making supported by real-time suggestions that are automatically generated by 3. Inference Engine. Rules and variables derived from the most relevant cardiovascular Clinical Practice Guidelines (CPGs) are introduced by cardiologists into the system. The rules undergo a process of peer review and internal and external validation with anonymized clinical cases. Results More than 2,000 clinical variables and more than 5,000 rules corresponding to more than 80 CPGs and consensuns papers have been created and validated. An internal and external pilot validation of the system has been successfully carried out for the main cardiovascular problems, such as heart failure, atrial fibrillation, valvulopathies, coronary syndromes, cardiomyopathies, hypertension, diabetes and dyslipidaemias, using more than 400 cases. Conclusion Our CDSS is capable of providing real-time diagnostic and therapeutic recommendations for complex scenarios in cardiovascular disease, combining thousands of variables and decision rules in a single system. Prospective evaluation of the system in real clinical environments, certification and integration in Electronic Medical Records for clinical use is required. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): DILEMMA solutions, SL

Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

Massive Open Online Courses (MOOCs) provide learners with high-quality and flexible online courses with no limitations regarding time and location. Detecting users’ behavior in MOOCs is an important task for course recommendations. Collaborative Filtering (CF) is considered the widely approach in recommender systems to provide a online learner courses according to similar learners’ preferences in an e-learning environment. The current research provides a novel framework through machine learning techniques to propose course recommendations in MOOCs according to the uses’ preferences and behavior. The method is developed using multi-criteria ratings extracted from users’ online reviews. We use Latent Dirichlet Allocation (LDA) for text mining, Decision Trees for decision rule generation, Self-Organizing Map (SOM) for users’ reviews on courses and the fuzzy rule-based system for users’ preferences prediction. We also adopt a feature selection method to select the most important criteria for users’ preferences prediction. The method is evaluated using the data collected from an online learning platform, Udemy. The results showed that the method is able to accurately provide relevant courses to the users tailored to their preferences. The method has the potential to be implemented as a recommendation agent in the MOOC websites for course recommendations.

Estimation of raw silk quality using rough set theory

Being an agro based natural product, silk always poses vagueness in the parameters. Hence, formulation of decision rules for prediction of raw silk quality from the imprecise cocoon parameters is an intricate problem. Rough set theory has evolved as one of the most important technique used for handling imprecise data. One of the cardinal uses of rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for the applications of rough set theory. Our approach focuses on the elimination of the redundant data set in order to generate the effective decision rule which retain the accuracy of the original data set. In this work rough set theory is employed to generate decision rules to predict renditta from five cocoon parameters such as shell ratio, defective cocoon percentage, cocoon volume, cocoon weight and filament length.

Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

Redistribution under general decision rules

AbstractWe study the political economy of redistribution over a broad class of decision rules. We suggest a simple and elegant procedure to select a robust equilibrium from the multiplicity in the core. Equilibrium policy depends on the full income profile, and, importantly, the preferences of two decisive voters. We show that the effect of increasing inequality depends on the decision rule and the shape of the income distribution; redistribution will increase if both decisive voters are “relatively poor,” and decrease if at least one is sufficiently “rich.” Additionally, redistribution decreases as the polity adopts increasingly stringent super‐majority rules.

A multiple combined method for rebalancing medical data with class imbalances

Most classification algorithms assume that classes are in a balanced state. However, datasets with class imbalances are everywhere. The classes of actual medical datasets are imbalanced, severely impacting identification models and even sacrificing the classification accuracy of the minority class, even though it is the most influential and representative. The medical field has irreversible characteristics. Its tolerance rate for misjudgment is relatively low, and errors may cause irreparable harm to patients. Therefore, this study proposes a multiple combined method to rebalance medical data featuring class imbalances. The combined methods include (1) resampling methods (synthetic minority oversampling technique [SMOTE] and undersampling [US]), (2) particle swarm optimization (PSO), and (3) MetaCost. This study conducted two experiments with nine medical datasets to verify and compare the proposed method with the listing methods. A decision tree is used to generate decision rules for easy understanding of the research results. The results show that (1) the proposed method with ensemble learning can improve the area under a receiver operating characteristic curve (AUC), recall, precision, and F1 metrics; (2) MetaCost can increase sensitivity; (3) SMOTE can effectively enhance AUC; (4) US can improve sensitivity, F1, and misclassification costs in data with a high-class imbalance ratio; and (5) PSO-based attribute selection can increase sensitivity and reduce data dimension. Finally, we suggest that the dataset with an imbalanced ratio >9 must use the US results to make the decision. As the imbalanced ratio is < 9, the decision-maker can simultaneously consider the results of SMOTE and US to identify the best decision.