Rule-based Classification Algorithm Research Articles

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 Decision Rule-Based Online Classification Algorithm in Fuzzy Formal Decision Contexts

Formal concept analysis (FCA) extracts interpretable rules by using implication relationships between concepts, which is an effective method for acquiring knowledge. In this paper, a fuzzy decision rule-based online classification algorithm called OFRCA is designed by combining online learning theory and FCA in a fuzzy formal decision context. First, the original weight vector of all attributes is obtained by fusing the original deterministic decision rules. Second, incremental fuzzy decision rules are obtained according to the added objects. Then for the incremental fuzzy decision rules, OFRCA updates the weight vector based on minimizing the loss function. At the end of the process, all the rules are fused into an attributeweighted classifier with the final weight vector. In this paper, we employ the regret to obtain a learning guarantee for OFRCA. The growth rate of OFRCA's regret converges to 0 as the number of iterations increases in the ideal state, which provides an effective learning guarantee for OFRCA. To verify the effectiveness of the proposed algorithm, this paper conducted numerical experiments and systematically analyzed the experimental results, which demonstrated that OFRCA achieved the highest advanced classification performance among the compared algorithms.

Supervised discretization of continuous-valued attributes for classification using RACER algorithm

In the contemporary world, data pervades every facet of human life, and the information contained in this data plays a pivotal role in shaping decision-making and advancing technology. Among the plethora of techniques available, classification methods are highly effective tools for extracting valuable insights from vast volumes of data. The Rule Aggregation ClassifiER (RACER) is a novel rule-based classification algorithm known for its exceptional performance. A notable limitation of RACER lies in its inability to handle continuous features. In this paper, we address the aforementioned limitation by employing various supervised discretization methods, including CAIM, MDLP, Decision Tree (CART), and ChiMerge. The impact of these methods on RACER’s accuracy and understandability is evaluated across nine datasets from the UCI repository. Additionally, the paper conducts a comparative analysis of RACER’s accuracy against well-known classifiers such as Naive Bayes, Logistic Regression, SVM, LightGBM, and Decision Tree. The findings indicate that RACER achieves the highest average accuracy when we utilize MDLP as the discretization method, surpassing its initial average accuracy. Moreover, RACER demonstrates superior understandability by generating the lowest number of rules when employing ChiMerge and Decision Tree for discretizing numerical features. Furthermore, RACER outperforms the other five classifiers when employing MDLP.

Developing a Classification Algorithm for Prediabetes Risk Detection From Home Care Nursing Notes: Using Natural Language Processing.

This study developed and validated a rule-based classification algorithm for prediabetes risk detection using natural language processing from home care nursing notes. First, we developed prediabetes-related symptomatic terms in English and Korean. Second, we used natural language processing to preprocess the notes. Third, we created a rule-based classification algorithm with 31 484 notes, excluding 315 instances of missing data. The final algorithm was validated by measuring accuracy, precision, recall, and the F1 score against a gold standard testing set (400 notes). The developed terms comprised 11 categories and 1639 words in Korean and 1181 words in English. Using the rule-based classification algorithm, 42.2% of the notes comprised one or more prediabetic symptoms. The algorithm achieved high performance when applied to the gold standard testing set. We proposed a rule-based natural language processing algorithm to optimize the classification of the prediabetes risk group, depending on whether the home care nursing notes contain prediabetes-related symptomatic terms. Tokenization based on white space and the rule-based algorithm were brought into effect to detect the prediabetes symptomatic terms. Applying this algorithm to electronic health records systems will increase the possibility of preventing diabetes onset through early detection of risk groups and provision of tailored intervention.

An object-based sparse representation model for spatiotemporal image fusion

Many algorithms have been proposed for spatiotemporal image fusion on simulated data, yet only a few deal with spectral changes in real satellite images. An innovative spatiotemporal sparse representation (STSR) image fusion approach is introduced in this study to generate global dense high spatial and temporal resolution images from real satellite images. It aimed to minimize the data gap, especially when fine spatial resolution images are unavailable for a specific period. The proposed approach uses a set of real coarse- and fine-spatial resolution satellite images acquired simultaneously and another coarse image acquired at a different time to predict the corresponding unknown fine image. During the fusion process, pixels located between object classes with different spectral responses are more vulnerable to spectral distortion. Therefore, firstly, a rule-based fuzzy classification algorithm is used in STSR to classify input data and extract accurate edge candidates. Then, an object-based estimation of physical constraints and brightness shift between input data is utilized to construct the proposed sparse representation (SR) model that can deal with real input satellite images. Initial rules to adjust spatial covariance and equalize spectral response of object classes between input images are introduced as prior information to the model, followed by an optimization step to improve the STSR approach. The proposed method is applied to real fine Sentinel-2 and coarse Landsat-8 satellite data. The results showed that introducing objects in the fusion process improved spatial detail, especially over the edge candidates, and eliminated spectral distortion by preserving the spectral continuity of extracted objects. Experiments revealed the promising performance of the proposed object-based STSR image fusion approach based on its quantitative results, where it preserved almost 96.9% and 93.8% of the spectral detail over the smooth and urban areas, respectively.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

New Discrete Crow Search Algorithm for Class Association Rule Mining

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

Monitoring the dynamics of Bara Shigri glacier using Synthetic Aperture Radar data of Sentinel-1 satellite and effect of climate on mass balances

A decision rule based classification algorithm is developed to classify the transient glacial zones of Bara Shigri glacier during the end of the ablation periods. The method helps to identify the highest snowlines and mean altitudes which further used to estimate mass balances of the glacier. The mass balance values of the Bara Shigri glacier are −1.37, −1.7, −0.77 and −1.07 m w.e.a−1 for the years 2017–2020 respectively. These values are compared with past mass balance values available for last four decades. A steady rise of mass loss is prominent and the current derived values are falling in that trend. The data of precipitation and land surface temperature during the study period are also studied to understand the controlling force of mass balance and it shows that a heavy precipitation during end of the ablation season has an indirect relation with mass loss amount. Deriving mass balance of the glacier is not a complete analysis until it can be relate with prevailing weather conditions and climatic variability in long term.

Patients Data Extraction from DDO Files Extension using Rule-based Classification Algorithm

Arrangement patient’s report process of Elim Rantepao hospital is especially complicated because patient data was obtained from FCR Console Prima what saved as DDO file extension as Unicode string. This problem makes understanding and select data manually become difficult. This research tries to give a solution on how to arrange reports easily through application tools developing. This research uses a rule-based classification algorithm to extract Patient Name, Age, ID Number, Date, and time. Applying this algorithm, increase system performance up to 96,6%. This application is very accurate as long as the file DDO has not broken.

Coverage-Based Classification Using Association Rule Mining

Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.

ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based “white-box” classification algorithm for medical data

BackgroundMulti-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay. MethodsApplied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection. A risk score was then computed from each stay according to the triggered rules.Recall and precision curves were plotted. ResultsThe classification can be focused on specifically detecting high risk of having a positive test, or identifying large numbers of at-risk patients by modulating the risk score cut-off level. For a risk score above 0.85,recall (sensitivity) is 62 % with 69 % precision (confidence) for MDR bacteria, recall is 58 % with 88 % precision for MRSA. In addition, MOCA-I identifies 38 and 21 cases of previously unknown MDR and MRSA respectively. ConclusionsMOCA-I generates medically pertinent alert rules. This classification algorithm can be used to detect patients with high risk of testing positive to MDR bacteria (including MRSA). Classification can be modulated by appropriately setting the risk score cut-off level to favor specific detection of small numbers of patients at very high risk or identification of large numbers of patients at risk. MOCA-I can thus contribute to more adapted treatments and preventive measures from admission, depending on the clinical setting or management strategy.

A real-time walking pattern recognition method for soft knee power assist wear

Real-time recognition of walking-related activities is an important function that lower extremity assistive devices should possess. This article presents a real-time walking pattern recognition method for soft knee power assist wear. The recognition method employs the rotation angles of thighs and shanks as well as the knee joint angles collected by the inertial measurement units as input signals and adopts the rule-based classification algorithm to achieve the real-time recognition of three most common walking patterns, that is, level-ground walking, stair ascent, and stair descent. To evaluate the recognition performance, 18 subjects are recruited in the experiments. During the experiments, subjects wear the knee power assist wear and carry out a series of walking activities in an out-of-lab scenario. The results show that the average recognition accuracy of three walking patterns reaches 98.2%, and the average recognition delay of all transitions is slightly less than one step.

PRRAT_AM—An advanced ant-miner to extract accurate and comprehensible classification rules

Ant-Miner, a rule-based classification algorithm, has been successfully applied for classification tasks but it has some limitations such as getting stuck in local optima, high selective pressure, fixed exploration and exploitation rate, and premature convergence. In this paper, we have proposed a novel Ant-Miner based technique based on new Pheromone update method, Rule Rejection threshold, Adaptive gamma, and altered Tournament selection (PRRAT_AM) that caters to these limitations. The proposed algorithm introduced an adaptive gamma parameter to avoid fixed exploration and exploitation rate. To decrease the selective pressure, pheromone is updated by weighted average of rule length, rule quality and heuristic of the path. Ants are selected using improved tournament selection strategy to update the pheromone. Rules that covered less than one percent of the training examples are rejected to generate generic rules. These improvements aid PRRAT_AM in avoiding premature convergence and high selective pressure. We have tested the proposed approach on eight publicly available data-sets on standard benchmark performance measures that include accuracy and F1-score. The proposed approach has been compared with state of the art versions of Ant-Miner and with various data mining algorithms. The experimental results showed that the proposed approach achieved better results when compared with other techniques in terms of standard performance measures and convergence speed.

A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students

This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor. The outcomes show that this approach presents better accuracy according to the progress of students, mainly when the JRip and PART algorithms are used. Furthermore, the use of this method enabled the generation of rules that stress the factors that mainly affect the dropout phenomenon at different degree moments.

Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art.

Airborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape

Rapid decline and death of rural Eucalypts trees of all ages and species have been reported in the farmscapes of regional Australia due to various environmental and farming management related factors. The identification of existing farm tree species is important for long term management strategies to provide ecosystem stability in the region. This study explored the feasibility of structural attributes of LiDAR and spectral and spatial characteristics of high resolution remote sensing data to identify and map Eucalyptus tree species. An object based image segmentation and rule-based classification algorithm were developed to delineate tree boundaries and species classification. The integration of two datasets improved the classification accuracy (65%) against their separate classification (52% and 41%, respectively). The identification of tree species will help in getting first-hand information on existing farm trees, which may be used in assessing tree condition in time series related to management practices and complex dieback problem.

CBA_Optimized: An Optimized Association based Classification Model with Enhanced Classification Accuracy Rate

Association classification is now evolving as an efficient data mining technique for building the classification models with enhanced accuracy rate. Many studies proposed different pruning methods to obtain highly efficient classifier rules in order to enhance the accuracy of the classifier. The pruning strategy must lead to reduced number of high quality rules that contribute to the accurate classification of the data. This study presents the improved version of association rule based classification algorithm CBA (Classification based on Association ) called CBA_Optimized that attempts to optimize the rules discovered by rule generation process used in CBA. The CBA_Optimized uses a unique ranking process to order the occurrence of the rules based on high confidence and rule class frequency to select the classifier rule to classify the data. This paper presents the efficient pruning method and extends the unique single rule classification strategy of CBA being more simple and accessible AC algorithms. With reduced size and slightly faster processing speed than other AC algorithms, the study attempts to enhance the classification accuracy rate.

Designing Weather Based Crop Insurance Payout Estimation Based on Agro-Meteorological Data using Machine Learning Techniques

Data mining techniques have been extensively used to mine up-to-date information from agricultural databases. In Agriculture, the Loss Assessment and Estimation in Crop insurance can be done on various factors like yield-based, crop-health based and weather-based variations. Weather-based variations are taken into account to design the insurance payout classifier model for the selected crop within the selected agricultural blocks of Tamilnadu. Then the weather attributes that undergone feature selection are given as input to the model with the rule-based classification algorithm implementing the neighboring approach with a sequential covering strategy named as CBKNN-PAYRULE which is statistically higher than other state-of-the-art rule-based classification algorithms. This model is proposed to classify the agricultural blocks based on the Area-wise Assessment of adverse temperature for the groundnut crop from their nearest neighbor. Then By combining the classified neighboring approach with the threshold factors the Rule-based classifier is done to generate the rules to estimate the insurance payout value as per policymakers for the selected agricultural blocks. Then decision-making techniques are applied to predict the insurance with the possibility of product basis risk, which covers the deviations in weather indices with the risk profile factors for the notified agricultural blocks for the specified crop. Thus the proposed technique can support the simultaneous prediction of the insurance payout to be paid in case of adverse weather factors of the selected crop for five districts with high accuracy and the correlation analysis of weather factors with the payout concerning to each district is also made. The Experimental results show that the proposed work enhances the accuracy in insurance payout prediction of the groundnut crop of the selected districts.

An Extended C4.5 Classification Algorithm using Mathematical Series

The algorithm C4.5 is an efficient decision tree based classification, which is derived from the ID3 approach. C4.5 is also a rule based classification algorithm. The main importance of the C4.5 algorithm is that it can deal with categorical data, over fitting of data and handling of missing values. The performance of C4.5 is superior to ID3 even with equal number of attributes. The EC4.5 (Exponential C4.5) is an extension of C4.5 algorithm which uses exponential of split value to predict the gain of attributes and handled the set back reported in C4.5. However the EC4.5 has some misclassification of data and to avoid this problem a new technique is introduced. This paper proposes a proficient technique TMC4.5 (Taylor-Madhava C4.5) to reduce the uncertainty in classification of data by integrating an exponential split value in EC4.5 and sin splitting value derived from the Madhava series. By using this technique an optimized gain value is obtained that reduces uncertainty. From the obtained result the TMC4.5 has far better results than the C4.5 and EC4.5 algorithms.