Rule-based Classification Model Research Articles

Machine learning reveals the rules governing the efficacy of mesenchymal stromal cells in septic preclinical models

BackgroundMesenchymal Stromal Cells (MSCs) are the preferred candidates for therapeutics as they possess multi-directional differentiation potential, exhibit potent immunomodulatory activity, are anti-inflammatory, and can function like antimicrobials. These capabilities have therefore encouraged scientists to undertake numerous preclinical as well as a few clinical trials to access the translational potential of MSCs in disease therapeutics. In spite of these efforts, the efficacy of MSCs has not been consistent—as is reflected in the large variation in the values of outcome measures like survival rates. Survival rate is a resultant of complex cascading interactions that not only depends upon upstream experimental factors like dosage, time of infusion, type of transplant, etc.; but is also dictated, post-infusion, by intrinsic host specific attributes like inflammatory microniche including proinflammatory cytokines and alarmins released by the damaged host cells. These complex interdependencies make a researcher’s task of designing MSC transfusion experiments challenging.MethodsIn order to identify the rules and associated attributes that influence the final outcome (survival rates) of MSC transfusion experiments, we decided to apply machine learning techniques on manually curated data collected from available literature. As sepsis is a multi-faceted condition that involves highly dysregulated immune response, inflammatory environment and microbial invasion, sepsis can be an efficient model to verify the therapeutic effects of MSCs. We therefore decided to implement rule-based classification models on data obtained from studies involving interventions of MSCs in sepsis preclinical models.ResultsThe rules from the generated graph models indicated that survival rates, post-MSC-infusion, are influenced by factors like source, dosage, time of infusion, pre-Interleukin-6 (IL-6)/ Tumour Necrosis Factor- alpha (TNF-α levels, etc.ConclusionThis approach provides important information for optimization of MSCs based treatment strategies that may help the researchers design their experiments.

Single sample-oriented attribute reduction for rule learning with formal concept analysis

As an effective tool for data mining, formal concept analysis can yield interpretable decision rules using attribute reduction. Currently, existing reduction methods within the framework of formal concept analysis result in all samples sharing the same conditional attributes, thereby overlooking the distinctions between rules extracted from different samples. To address this problem, we present a novel attribute reduction method for single samples. For this purpose, a single sample-oriented reduction framework is established by incorporating a discernibility attribute vector and an algorithm for calculating reducts based on the matrix representation of the discernibility attribute vector. Furthermore, the process of obtaining a new reduct from the original reduct is discussed by considering incremental learning. Given that attributes included in reducts are crucial conditions for decision-making, intent-minimal granular rules are generated through attribute reduction, and a reduction-based method for measuring the completeness of the rule set is discussed. Finally, to accomplish the classification task, a minimal rule-based classification model (MRCM) is proposed. The experimental results show that the MRCM has the same classification ability as the unreduced rules, and the rules with higher completeness tend to exhibit better classification performance.

Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients.

Explainable Artificial Intelligence (XAI) is becoming a disruptive trend in healthcare, allowing for transparency and interpretability of autonomous decision-making. In this study, we present an innovative application of a rule-based classification model to identify the main causes of chronic cough-related quality of life (QoL) impairment in a cohort of asthmatic patients. The proposed approach first involves the design of a suitable symptoms questionnaire and the subsequent analyses via XAI. Specifically, feature ranking, derived from statistically validated decision rules, helped in automatically identifying the main factors influencing an impaired QoL: pharynx/larynx and upper airways when asthma is under control, and asthma itself and digestive trait when asthma is not controlled. Moreover, the obtained if-then rules identified specific thresholds on the symptoms associated to the impaired QoL. These results, by finding priorities among symptoms, may prove helpful in supporting physicians in the choice of the most adequate diagnostic/therapeutic plan.

A Hybrid Metaheuristic Algorithm Using Elitist Chemical Reaction Optimization and Learning from Knowledge Assimilation for Improving Rule-based Classification Models

There are various paradigms of learning. One such paradigm is learning from knowledge assimilation. In machine learning, such a paradigm may be considered where concepts (classification models) generated from different algorithms applied to the same training data are combined to come up with a single integrated improved concept (classification model). This is different from combining outputs of various models as in Bagging or Boosting. Thus, learning from knowledge assimilation creates different alternative models that work on different parts of the search space. Such models can be separately optimized so that the best-performing model can be selected for the given problem. For optimization there are several algorithms and Chemical Reaction Optimization (CRO) is a chemistry-based nature-inspired optimization algorithm. In this algorithm, a set of optimization approaches are introduced corresponding to various types of chemical reactions. Classification models in our approach are optimized using a technique called Elitist CRO(ECRO). ECRO is an improvement over CRO, where the optimization approaches are modified making the convergence of the algorithm better than CRO. Although for classification CRO models based on neural networks exist, however, they suffer from interpretability. This paper proposes an application of a new Hybrid Elitist Chemical Reaction Optimization (HECRO) algorithm to generate interpretable classification rules. The proposed algorithm is an extension of CRO and ECRO, in the manner of region exploration of the search space, which is expanded by combining classification rules generated from various algorithms as the initial population following learning from knowledge assimilation. Experimental results with 20 datasets drawn from the UCI Machine Learning data repository show improvement over several performance measures in comparison to the base classifiers used for population generation in HECRO.

Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models

Introduction: Iron deficiency anemia is the most common cause of anemia worldwide, and increased iron requirement during pregnancy increases the risk of anemia. Anemia in pregnancy is associated with adverse pregnancy outcomes such as low birth weight, preterm and intrauterine growth restriction. This study used a Rule-based Intelligent Classification Models to predict socio-demographic, nutritional, antenatal care and obstetric factors on iron deficiency anemia during pregnancy Methods: This retrospective study was a secondary analysis of a community-based cross-sectional study conducted between January and June 2019 in the province of Elazig in eastern Turkey. Data of 495 pregnant women were included in the study iron deficiency anemia was defined as hemoglobin < 11 g/dl, and ferritin < 30 µg/L. Rule-based machine learning methods were used to predict factors associated with anemia during pregnancy. Results: The mean age of 495 pregnant women were 30.06 ± 5.15 years. The prevalence of anemia was 27.9% in study population. Maternal age, educational status, occupation, nutrition education status, nutritional property, gravida, and parity were significantly related to anemia. Jrip, OneR, and PART algorithms estimated factors associated with anemia with 96.36%, 85.45%, and 97.98% accuracy, respectively. Conclusion: Rule-based machine learning algorithm may offer a new approach to risk factors for iron deficiency anemia during pregnancy. With the use of this model, it is possible to predict the risk of anemia both before and during pregnancy and to take preventative measures.

Concise rule induction algorithm based on one-sided maximum decision tree approach

As the importance of machine learning tools for decision support continues to grow, interpretability has emerged as a key factor. Rule-based classification algorithms, such as decision trees and rule induction, enable high local interpretability by providing transparent reasoning rules in an IF-THEN format. In this context, it is essential to provide concise and clear rules and conditions to achieve high local interpretability. This study proposes a novel Concise Algorithm, designed to effectively remove irrelevant conditions from classification rules. We present a framework incorporating the Concise Algorithm, which employs the One-Sided-Maximum decision tree algorithm for rule generation, followed by the application of the Concise Algorithm to remove irrelevant conditions. This proposed framework produces a rule-based classification model that exhibits an enhanced predictive performance-interpretability trade-off compared to benchmark methods (CART, Ripper, CN2, and modified One-Sided-Maximum), as demonstrated by empirical tests conducted on 19 UCI datasets. A case study focusing on the breast-cancer-wisconsin dataset provides a comprehensive analysis of the rule and condition generation processes.

Fuzzy Rule-Based Classification Method for Incremental Rule Learning

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance.

Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework

Abstract Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification (MRSFS-RMDC) technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction (RIPPER) is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%.

Enriched intuitionistic fuzzy rule pruning using butterfly optimization for rule pruning in intrusion detection

The process of detecting and reacting to hostile activities against the computer and network resources is known as intrusion detection. Rather of relying just on expertise and experience, a systematic and automated IDS creation process is required. This drives researchers to look into data mining-based intrusion detection frameworks. Though there are many algorithms are developed for detection intrusion, the issue of inconsistency in rule-based classification model is controlling the volume of data and removing irrelevant rules are the major problems focused in this work to improve the process of IDS. This paper proposed a framework which handles the hesitancy in classifying the unknown traffic pattern data packets by designing evolutionary algorithm optimized intuitionistic fuzzy inference system. The Intuitionistic fuzzy inference system represents each instances of KDD cup 99 dataset with the degree of membership and non-membership. With these two degrees, the hesitancy degree is computed to overcome the issue of inconsistency and uncertainty in analysing the unknown pattern of abnormal packets. In conventional Intuitionistic fuzzy System (IFS), the rules generated by the inference engine is applied for classification directly without determine the validation of it.

A data-driven medical knowledge discovery framework to predict the length of ICU stay for patients undergoing craniotomy based on electronic medical records.

Craniotomy is an invasive operation with great trauma and many complications, and patients undergoing craniotomy should enter the ICU for monitoring and treatment. Based on electronic medical records (EMR), the discovery of high-risk multi-biomarkers rather than a single biomarker that may affect the length of ICU stay (LoICUS) can provide better decision-making or intervention suggestions for clinicians in ICU to reduce the high medical expenses of these patients and the medical burden as much as possible. The multi-biomarkers or medical decision rules can be discovered according to some interpretable predictive models, such as tree-based methods. Our study aimed to develop an interpretable framework based on real-world EMRs to predict the LoICUS and discover some high-risk medical rules of patients undergoing craniotomy. The EMR datasets of patients undergoing craniotomy in ICU were separated into preoperative and postoperative features. The paper proposes a framework called Rules-TabNet (RTN) based on the datasets. RTN is a rule-based classification model. High-risk medical rules can be discovered from RTN, and a risk analysis process is implemented to validate the rules discovered by RTN. The performance of the postoperative model was considerably better than that of the preoperative model. The postoperative RTN model had a better performance compared with the baseline model and achieved an accuracy of 0.76 and an AUC of 0.85 for the task. Twenty-four key decision rules that may have impact on the LoICUS of patients undergoing craniotomy are discovered and validated by our framework. The proposed postoperative RTN model in our framework can precisely predict whether the patients undergoing craniotomy are hospitalized for too long (more than 15 days) in the ICU. We also discovered and validated some key medical decision rules from our framework.

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.

A dynamic rule-based classification model via granular computing

As an effective tool for data representation and processing, granular computing has been incorporated into formal decision contexts for finding granular reducts to achieve the task of mining granular rules. However, the classification performance of granular rules has not been evaluated, and this type of method is not suitable for dynamic data. To solve this problem, the current study updates granular reducts and evaluates the obtained granular rules in terms of classification performance. Concretely, we first give a theoretical analysis of updating granular reducts and granular rules and then present a novel dynamic rule-based classification model (DRCM) based on the updating mechanism. Finally, we discuss the feasibility of the proposed model and compare it with several popular classification algorithms. The conducted experiments demonstrate that the granular reducts can improve the classification ability to a certain extent and that DRCM can achieve better classification performance on some consistent datasets.

Global Trends in Plastic Surgery on Social Media: Analysis of 2 Million Posts.

Plastic surgeons and patients increasingly use social media. Despite evidence implicating its importance in plastic surgery, the large volume of data has made social media difficult to study. The aim of this study was to provide a comprehensive assessment of plastic surgery social media content worldwide by utilizing techniques for analyzing large-scale data. The hashtag "#PlasticSurgery" was used to search public Instagram posts. Metadata were collected from posts between December 2018 and August 2020. In addition to descriptive analysis, 2 instruments were created to characterize textual data: a multilingual dictionary of procedural hashtags and a rule-based text classification model to categorize the source of the post. Plastic surgery content yielded more than 2 million posts, 369 million likes, and 6 billion views globally over the 21-month study. The United States had the most posts of 182 countries studied (26.8%, 566,206). Various other regions had substantial presence including Istanbul, Turkey, which led all cities (4.8%, 102,208). The classification model achieved high accuracy (94.9%) and strong agreement with independent raters (κ = 0.88). Providers accounted for 40% of all posts (847,356) and included the categories physician (28%), plastic surgery (9%), advanced practice practitioners and nurses (1.6%), facial plastics (1.3%), and oculoplastics (0.2%). Content between plastic surgery and non-plastic surgery groups demonstrated high textual similarity, and only 1.4% of posts had a verified source. Plastic surgery content has immense global reach in social media. Textual similarity between groups coupled with the lack of an effective verification mechanism presents challenges in discerning the source and veracity of information.

Extracting revisit intentions from social media big data: a rule-based classification model

Purpose The examination of revisit intentions in hospitality is integral to relationship marketing and customer loyalty. Its measurement and determination have largely been done through closed-ended measures in surveys of customers. However, vast troves of consumer-generated media in the form of open-ended text reviews can also serve as sources for the determination of revisit intentions. The purpose of this paper is to develop and test a rule-based classification model from big data to extract revisit intentions. Design/methodology/approach Data for this came from 116,241 reviews scraped from Tripadvisor.com using a stratified sampling technique comprising hotels in major cities in the USA. A sample comprising 1,800 reviews was randomly drawn from this larger pool of reviews and manually annotated. A manual-set rule-based model, supervised machine learning (ML) models and hybrid models were developed to extract revisit intention. Findings The hybrid model of the MSRB method complemented by the gradient boosting ML method performed the best to classify revisit intentions in reviews. Practical implications This study’s rule-based classification model can be used by hotels to evaluate revisit intentions from the ever-growing pool of consumer-generated reviews. This can enable hotels to identify drivers of re-patronage and enhance relationship marketing initiatives. Originality/value This study is the first to propose an analytical model that taps big data to extracting revisit intentions. In the past, revisit intentions have been assessed using closed-ended questions using traditional survey-based methods.

An efficient algorithm for unique class association rule mining

Association rule mining is one of the main means in Knowledge discovery and Machine learning. Such kind of rules present knowledge of interrelations among items in a dataset. Class Association Rules (CARs) are a subset of association rules which are always mined using labeled datasets. Simply, a typical CAR has an itemset that is associated to a class label. Mining CARs is vital for construction of pattern or rule-based classification models and has received recently increasing research interest. In this work, a complete efficient but not exhaustive CAR mining algorithm (UniqAR) is introduced. UniqAR generates always and only 100% accurate CARs which are called unique association rules using two rule search hypothesis of Subsumption and Nonsense to find unique itemsets in order to generate the Unique CARs. Unlike alternatives of CAR mining algorithms, UniqAR mined association rules aren’t based on itemset frequency or item selectivity. It can generate both frequent and rare association rules. No preferences of support, coverage, or item participant in itemsets are required to be provided for the proposed mining process. The main contribution of this work to CARs’ state of the art is describing unique itemsets and class association rules and providing an efficient mining process for them. Unlike the other unique rule mining alternatives in the literature, the proposed novel mining process depends on a complete but not exhaustive search that employs rules inter-relations. UniqAR has been modeled with computational analysis and extended evaluation. It is shown that UniqAR can extract all unique itemsets for unique association mining with no need to setup any user preferences, template or any constraints. Moreover, it describes accurately the effects of different dataset criteria like number of attributes/features, feature values, cases, and class labels on UniqAR unique itemset extraction mining process in an efficient way that avoids a huge number of itemsets/cases comparisons. Results have shown that the proposed UniqAR algorithm is feasible and promising.

Enhanced Hyperbox Classifier Model for Nanomaterial Discovery

Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis.

A Data Mining Approach to Prediction of Liver Diseases

One of the major diseases in the world is liver disease. The liver is an important part of the large organs in the human body and is also considered a gland. This is because it creates and bites bile. Liver disease is a liver problem that causes the disease. The objective of this study is to propose a rule-based classification model with basic decision making techniques to predict various types of heart disease. To get better results the experiment was done using a different data mining algorithm compared with previous liver disease predictions. All experiments have been implemented at Azure Machine Learning tool. This paper is about to study the prediction of liver disease to produce better performance accuracy by comparing various mining data classification algorithms.

Rule-based granular classification: A hypersphere information granule-based method

As fundamental abstract constructs supporting the human-centered way of Granular Computing (GrC), information granules can be used to distinguish different classes of data from the perspective of easily understood geometrical structure. In this study, a three-stage rule-based granular classification method is proposed using a union of a series of hypersphere information granules. The first stage focuses on dividing each class of data into a series of chunks. The second stage concerns the construction of some hyperspheres around these chunks. These resulting hyperspheres form a union information granule to depict the key structural characteristics of the corresponding data through their union operation. At the final stage, the union information granules are refined and the rule-based granular classification model is emerged through using a series of “If-Then” rules to articulate the refined union information granule formed for each class with the corresponding class label. A number of experiments involving several synthetic and publicly available datasets are implemented to exhibit the advantages of the resulting classifier. The impacts of critical parameters on the performance of the constructed classifier are also revealed.

Creating Eco-Innovations for Sustainable Entrepreneurship and Development within European Countries

There is no doubt that innovations represented key source of competitive advantage in the last years. Therefore, firms were forced to come up with new or significantly improved goods, services as well as organizational and marketing practices. However, countries, firms and policy makers are facing great environmental challenges for sustainable development and entrepreneurship, which create large demand for Eco-innovations with environmental benefits nowadays. For these reasons, we are analyzing different factors influencing creation of firms´ Eco-innovations in the EU. To obtain an interpretable model of firms´ Eco-innovations and their determinants, we use fuzzy rule-based classification models and latest available data form Community Innovation Survey 2012-2014 created by Eurostat. Our results show that firms´ Eco-innovations are influenced by different factors according to the nature of innovation (product, process, organizational, marketing) in the EU. Public financial subsidies and firms´ environment (e.g. existing environmental regulations, taxes, charges or fees, market demand, and costs of inputs) play key role in the process of creation Eco-innovation with environmental benefits. These results show possible ways how support sustainable entrepreneurship and regional (national) development not only within analyzed countries. We provide initial analysis dealing with different kinds of Eco-innovations and its determinants that could help firms to develop and enhance their corporate innovation and environmental policies. These results could also inspire policy makers at different levels because we propose some practical implications. Keywords: eco-innovations, sustainable development, environmental benefits, entrepreneurship

Semisupervised Classification Based Clustering Approach in WSN for Forest Fire Detection

Forest fire is a very considerable problem of ecological system. This paper depicts a novel technique which detects the high active(HA) zone (nearer to the epicenter of fire) in the forest and transmits all sensed data to the base station through wireless communication as early as possible. Fire office takes necessary action to prevent the spreading of fire. For this purpose sensors are deployed in forest zone to sense different data which are necessary for detecting forest fire and divides it into different clusters. A semisupervised rule-based classification model is proposed in this paper to detect whether its zone is high active, medium active (MA) or low active (LA) cluster in the forest. We train our proposed integrated model in such a way when only one parameter of sensed data is transmitted by the sensor nodes due to energy constraint to the initiator of that zone, initiator can be able to predict the state of (HA,MA,LA) zone with 96% accuracy. All the sensor nodes in HA cluster transmit their packet through cluster head to the base station continuously applying greedy forwarding technique. Authors consider energy saving strategy during cluster head selection and data transmission in HA zone. On the other hand, sensors in MA zone transmit packet periodically and LA zone avoids to transmit the sensed data. This way proposed technique transmits the sensed data from HA zone efficiently and quickly to forest office for forest fire prevention and saves the energy of all sensor nodes in the forest.