Large Number Of Rules Research Articles

Rule extraction based on PROMETHEE-assisted multi-objective genetic algorithm for generating interpretable neural networks

Rule extraction from artificial neural networks (ANNs) refers to the process of identifying the underlying decision-making logic used by the network in its predictions. This involves converting the complex, non-linear relationships between inputs and outputs in the network into a set of interpretable rules. The main objective of rule extraction is to enhance the interpretability of decision-making in artificial neural networks (ANNs) and make it more understandable for human experts, especially in critical domains like healthcare, where clear decision-making is essential. Despite the ability of machine learning methods to interpret ANNs, interpreting complex neural networks remains limited, as most studies tend to sacrifice one criterion, related to rule quality, in favor of another. Rule extraction methods applied directly to complex neural networks may produce overly specific rules that are tailored to the training data. Such rules can suffer from overfitting and may not generalize well to unseen data. Also, The use of multiobjective genetic algorithms for rule extraction can result in a large number of rules, making it challenging for decision-makers to interpret them. This study proposes an innovative approach to address challenges in rule extraction from complex artificial neural networks (ANNs) by integrating a multiobjective genetic algorithm with the PROMETHEE method. By integrating these techniques, the study aims to identify meaningful relationships between inputs and outputs of ANNs, generating concise and reliable rules that are ranked based on support, confidence, and lift values. The use of PROMETHEE's rule ranking mechanism enabled the consideration of rule priority during candidate selection, guiding the genetic algorithm towards discovering high-quality rule sets and efficiently exploring the search space. This approach not only enhances the quality of extracted rules but also facilitates efficient decision-making by striking a balance between rule accuracy, fidelity, and comprehensibility, thus contributing to advancing the understanding of complex neural networks. By equally weighting the criteria (support, confidence, and lift) during the rule ranking mechanism, our approach achieved high coverage rates ranging from 94.88 % to 100 % and generated a manageable number of rules that could be easily interpreted by human experts, ranging from 5 to 10.5. These findings demonstrate the potential of our approach to significantly improve decision-making accuracy and interpretability across various real-world applications, making it a promising tool for applications in healthcare, finance, and other fields.

An intelligence expert system for quality health monitoring of power transformers using testing attributes

Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in [Mharakurwa E T and Goboza R 2019 Multiparameter-based fuzzy logic health index assessment for oil-immersed power transformers Adv. Fuzzy Syst. 2019]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

Towards utilization of rule base structure to support fuzzy rule interpolation

AbstractFuzzy rule interpolation (FRI) offers a reliable approach for providing an interpretable approximate decision with a sparse rule base, when a new observation does not match any existing rules. As the mainstream application of a fuzzy rule base is to extract valuable approximate information from each individual rules, existing FRI methods typically work by postulating that the more rules used to implement the interpolation the better the reasoning outcomes. Yet, empirical results have shown that using a large number of rules in an FRI process may adversely lead to worsening the accuracy of the inference outcomes, not just degrading efficiency. The objective of this work is to set a firm theoretical foundation for the eventual establishment of a novel FRI approach. It achieves this goal by mapping the structural patterns within a given fuzzy rule base onto a mathematically isomorphic data space, such that the essential information embedded in the original rule base can be effectively captured, represented and analysed. The resulting mathematically mapped patterns enable the production of a theorem that determines the upper limit of the number of rules required to effectively and efficiently perform FRI. The experimental investigations reported herein demonstrate that the number of required rules to perform FRI obeys the theorem discovered in this work.

Design of a Multimedia-Assisted Distance English Teaching System for College Students.

To address the problems of poor compatibility, weak load capacity, and time consumption of existing multimedia-assisted teaching systems, we propose and design a physics multimedia-assisted English teaching system based on a concept network. The architecture of the physics multimedia-assisted teaching system is constructed with the logical layer as the core, and the theoretical knowledge module, experiment module, and student practice module are designed, respectively. Based on the concept network, a large number of rules in the physics teaching system are attached to the concepts to form the concept network knowledge system and complete the design of the theoretical knowledge system. This paper explores the multimedia teaching of college English and points out the advantages and characteristics of this teaching mode and the problems to be paid attention to in the process of application. The design of the physics multimedia-assisted teaching system is completed by realizing students' practice and giving feedback on the practice results. The experimental results show that the system has good compatibility, strong load capacity, low time consumption, and practical application value.

An Indexing Method of Continuous Spatiotemporal Queries for Stream Data Processing Rules of Detected Target Objects.

Real-time performance is important in rule-based continuous spatiotemporal query processing for risk analysis and decision making of target objects collected by sensors of combat vessels. The existing Rete algorithm, which creates a compiled node link structure for executing rules, is known to be the best. However, when a large number of rules are to be processed and the stream data to be performed are large, the Rete technique has an overhead of searching for rules to be bound. This paper proposes a hashing indexing technique for Rete nodes to the overhead of searching for spatiotemporal condition rules that must be bound when rules are expressed in a node link structure. A performance comparison evaluation experiment was conducted with Drool, which implemented the Rete method, and the method that implemented the hash index method presented in this paper. For performance measurement, processing time was measured for the change in the number of rules, the change in the number of objects, and the distribution of objects. The hash index method presented in this paper improved performance by at least 18% compared to Drool.

High performance modified bit-vector based packet classification module on low-cost FPGA

The packet classification plays a significant role in many network systems, which requires the incoming packets to be categorized into different flows and must take specific actions as per functional and application requirements. The network system speed is continuously increasing, so the demand for the packet classifier also increased. Also, the packet classifier's complexity is increased further due to multiple fields should match against a large number of rules. In this manuscript, an efficient and high performance modified bitvector (MBV) based packet classification (PC) is designed and implemented on low-cost Artix-7 FPGA. The proposed MBV based PC employs pipelined architecture, which offers low latency and high throughput for PC. The MBV based PC utilizes <2% slices, operating at 493.102 MHz, and consumes 0.1 W total power on Artix-7 FPGA. The proposed PC considers only 4 clock cycles to classify the incoming packets and provides 74.95 Gbps throughput. The comparative results in terms of hardware utilization and performance efficiency of proposed work with existing similar PC approaches are analyzed with better constraints improvement.

Model Simplification of Deep Random Forest for Real-Time Applications of Various Sensor Data.

The deep random forest (DRF) has recently gained new attention in deep learning because it has a high performance similar to that of a deep neural network (DNN) and does not rely on a backpropagation. However, it connects a large number of decision trees to multiple layers, thereby making analysis difficult. This paper proposes a new method for simplifying a black-box model of a DRF using a proposed rule elimination. For this, we consider quantifying the feature contributions and frequency of the fully trained DRF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified and transparent DRF has fewer parameters and rules than before. The proposed method was successfully applied to various DRF models and benchmark sensor datasets while maintaining a robust performance despite the elimination of a large number of rules. A comparison with state-of-the-art compressed DNNs also showed the proposed model simplification’s higher parameter compression and memory efficiency with a similar classification accuracy.

Visualizations for rule-based machine learning

Learning Classifier Systems (LCSs) are a group of rule-based evolutionary computation techniques, which have been frequently applied to data mining tasks. The LCSs’ rules are designed to be human-readable to enable the underlying knowledge to be investigated. However, the models for the majority of domains with high feature interaction contain a large number of rules that cooperatively represent the knowledge. However, the interaction between many rules is too complex to be comprehended by humans. Thus, it is hypothesized that translating the models’ underlying patterns into human-discernable visualizations will advance the understanding of the learned patterns and LCSs themselves. Interrogatable artificial Boolean domains with varying numbers of attributes are considered as benchmarks. Three new visualization techniques, termed as Feature Importance Map, Action-based Feature Importance Map, and Action-based Feature’s Average value Map, successfully produce interpretable results for all the complex domains tested. This includes both tracing the training progress and analyzing the trained models from LCSs. The visualization techniques’ ability to handle complex optimal solutions is observed for the 14-bits Majority-On problem, where the patterns from 6435 different cooperating rules were translated into human-discernable graphs.

An Improved Approach to Analyze Accidents and Promote Road Safety using Association Rule Mining and Multi-Criteria Decision Analysis Methods

Background: Road accidents have become a major social and health problem for being dramatically increasing day after day worldwide. Scientists are conducting their studies to define the main attributes that share the severity of road accidents. Finding a new approach to analyze road accidents is of great urgency. Data mining techniques are best fitting to discover useful information out of enormous data which are used to make proactive decisions. Methods: This paper tempts a rule-based machine learning method known as association rule mining, which can identify strong rules discovered in databases using interesting measures. Given a data- set from the Lebanese territory for the years 2016-2017, the application of association rule mining, the Apriori method takes its place. However, its implementation leads to a very large number of rules. The task that is the most difficult is extracting meaningful and non-redundant rules. In order to find out the most interesting and relevant rules out of fatal rules such, ELECTRE TRI and PROMETHEE methods, the most significant methods of decision making, Multi-Criteria Decision Analysis (MCDA) are integrated to resolve the outranking problem. The integration is presented by the use of the same set of weights and the same constant values of Indifference and Preference thresholds used in ELECTRE-TRI method to define the linear preference function needed by PROMETHEE method. Realizing the sensitivity of the final output of alternatives ranking to the changes of the input parameters of the decision-making tool, this proposed integration helps the decision makers to overcome their ambivalence between preference and indifference thresholds and to cope adequately with the issue of the uncertainty of MCDA procedures; it comes up with the complete ranking of rules. Results: The obtained ranked rules declare the most significant attributes or combinations of attributes that influence the severity of road accidents. Four main factors of fatal road accidents are pinpointed: over-speeding mainly leading up to rollover crashes, pedestrians encountering in the context, distracted driving leading to fatal road vehicle collisions with Pedestrian victims; and wet roads particularly in the case of single car accidents. Meanwhile, the importance of ELECRE-TRI and PROMETHEE and their integration in dealing with such complex phenomena and corresponding database with a large number of involved attributes have been validated. Conclusion: This paper studies the phenomenon of road accidents. Association rule mining has been applied to discover all possible relations between the various attributes. The integration of ELECTRE- TRI and PROMETHEE MCDA techniques aims at extracting meaningful information from the big dataset. The obtained results have shown how influencing the behavior of the driver is on the occurrence of fatal road accidents. These findings contribute to supporting decision makers to draw new design conceptions for road infrastructure and develop preventive measures that improve road safety in Lebanon.

Logical Rules and a Preliminary Prototype for Translating Mortality Coding Rules from ICD-10 to ICD-11.

Iris is a system for coding multiple causes of death in ICD-10 and for the selection of the underlying cause of death, based on a knowledge base composed by a large number of rules. With the adoption of ICD-11, those rules need translation to ICD-11. A pre-project has been carried out to evaluate feasibility of transition to ICD-11, which included the analysis of the logical meta-rules needed for rule translation and development of a prototype support system for the expert that will translate the coding rules.

THE PLACE OF ADMINISTRATIVE LAW STANDARDS IN THE LEGAL SYSTEM PRINCIPLES OF INTERACTION OF JUDICIAL EXPERT INSTITUTIONS WITH LAW ENFORCEMENT AUTHORITIES

The relevance of the article is that the effectiveness and efficiency of interaction between forensic agencies and law enforcement agencies depends on many factors, one of which is a properly "constructed" system of legal acts. At the same time, the special nature of the interaction of these entities, the attraction of its content to the administrative and legal sphere, as well as the specifics of forensic institutions and law enforcement agencies in general necessitates a detailed review of legal principles in this area and determining the place of administrative and legal regulation. The purpose of the article is to establish a system of legal bases for the interaction of forensic institutions with law enforcement agencies, as well as to determine the place of administrative and legal regulation among them. It is established that the legal basis of interaction of forensic institutions with law enforcement agencies is a system of regulations and their provisions governing the legal status of forensic institutions and law enforcement agencies, as well as the content and procedure of interaction of these entities. It is proved that administrative-legal regulation is a type of branch of the general-legal category of legal regulation, which occurs with the help of administrative law and determines the impact of law on public relations of a special nature arising from the activities of public administration. That is, we are talking about the relationship of power and management influence that prevails in the work of public authorities, local governments and so on. This is a purposeful, comprehensive, streamlining impact of law on public relations in the sphere of government, which occurs through the rules of administrative law, which are part of the system of legal principles outlined above. It is emphasized that the legal basis for the interaction of forensic institutions and law enforcement agencies have an administrative and legal basis, which is expressed in a large number of rules of administrative law, enshrined in regulations of various legal force. This situation is due to the fact that the norms of this branch of law determine: the administrative and legal status of forensic institutions and law enforcement agencies; functions, powers and tasks assigned to law enforcement agencies and forensic institutions; mechanisms of interaction of forensic institutions and law enforcement agencies in performing their functions defined by law; organizational and practical goals of this interaction; etc.

A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM

The task to understand systemic functioning and predict the behavior of today’s sociotechnical systems is a major challenge facing researchers due to the nonlinearity, dynamicity, and uncertainty of such systems. Many variables can only be evaluated in terms of qualitative terms due to their vague nature and uncertainty. In the first stage of our project, we proposed the application of the Functional Resonance Analysis Method (FRAM), a recently emerging technique, to evaluate aircraft deicing operations from a systemic perspective. In the second stage, we proposed the integration of fuzzy logic into FRAM to construct a predictive assessment model capable of providing quantified outcomes to present more intersubjective and comprehensible results. The integration process of fuzzy logic was thorough and required significant effort due to the high number of input variables and the consequent large number of rules. In this paper, we aim to further improve the proposed prototype in the second stage by integrating rough sets as a data-mining tool to generate and reduce the size of the rule base and classify outcomes. Rough sets provide a mathematical framework suitable for deriving rules and decisions from uncertain and incomplete data. The mixed rough sets/fuzzy logic model was applied again here to the context of aircraft deicing operations, keeping the same settings as in the second stage to better compare both results. The obtained results were identical to the results of the second stage despite the significant reduction in size of the rule base. However, the presented model here is a simulated one constructed with ideal data sets accounting for all possible combinations of input variables, which resulted in maximum accuracy. The same should be further optimized and examined using real-world data to validate the results.

A new insight into implementing Mamdani fuzzy inference system for dynamic process modeling: Application on flash separator fuzzy dynamic modeling

In this work, a novel approach to model the dynamic behavior of the flash separation process (as a main building block of non-reacting stage-wise operations) based on Mamdani Fuzzy Inference Systems is proposed. This model surmounts the need to solve various types of mathematical equations governing the system and does not require thermodynamic properties which are either not available or computationally demanding. Hence it can be easily used in dynamic simulation of multi-phase flow in distributed systems. In the proposed approach the overall model is broken into several simple sub-models based on intuitive analysis of an expert. Moreover, a new fuzzy concept, named “Linguistic Composition Variable” is introduced to represent components mole fractions of each phase as a fuzzy variable. Accordingly, large number of rules which is the main shortcoming of the Mamdani Fuzzy system is significantly reduced.The performance of the proposed dynamic model is evaluated through comparing its results against their corresponding counterparts for a flash separator of crude oil. Overall MAPE (Mean Absolute Percentage Error) values of 7.17% for the gas molar fractions, 3.06% for liquid molar fractions, 10.16% for the temperature, 0.63% for the pressure and 16.44% for the liquid level of the separator have been achieved showing that the proposed fuzzy model can effectively capture the general trends of process data of the dynamic process.

DART: a visual analytics system for understanding dynamic association rule mining

Dynamic rule mining can discover time-dependent association rules and provide more accurate descriptions about the relationship among items at different time periods and temporal granularities. However, users still face some challenges in analyzing and choosing reliable rules from the rules identified by algorithms, because of the large number of rules, the dynamic nature of rules across different time periods and granularities and the opacity of the relationship between rules and raw data. In this paper, we present our work on the development of DART, a visual analytics system for dynamic association rule mining, to help analysts gain a better understanding of rules and algorithms. DART allows users to explore rules at different time granularities (e.g., per hour, per day, per month, etc.) and with different time periods (e.g., daily, weekly, yearly, etc.), and to examine rules at multiple levels of detail, including investigating temporal patterns of a set of rules, comparing multiple rules, and evaluating a rule with raw data. Two case studies are used to show the functions and features of DART in analyzing business data and public safety data.

Incremental Server Deployment for Software-Defined NFV-Enabled Networks

Network Function Virtualization (NFV) is a new paradigm to enable service innovation through virtualizing traditional network functions. To construct a new NFV-enabled network, there are two critical requirements: minimizing server deployment cost and satisfying switch resource constraints. However, prior work mostly focuses on the server deployment cost, while ignoring the switch resource constraints (e.g., switch's flow-table size). It thus results in a large number of rules on switches and leads to massive control overhead. To address this challenge, we propose an incremental server deployment (INSD) problem for construction of scalable NFV-enabled networks. We prove that the INSD problem is NP-Hard, and there is no polynomial-time algorithm with approximation ratio of (1- ϵ)· ln m, where ϵ is an arbitrarily small value and m is the number of requests in the network. We then present an efficient algorithm with an approximation ratio of 2 · H(q · p), where q is the number of VNF's categories and p is the maximum number of requests through a switch. We evaluate the performance of our algorithm with experiments on physical platform (Pica8), Open vSwitches, and large-scale simulations. Both experimental results and simulation results show high scalability of the proposed algorithm. For example, our solution can reduce the control and rule overhead by about 88% with about 5% additional server deployment, compared with the existing solutions.

A Verified Session Protocol for Dynamic Service Chaining

Middleboxes are crucial for improving network security and performance, but only if the right traffic goes through the right middleboxes at the right time. Existing traffic-steering techniques rely on a central controller to install fine-grained forwarding rules in network elements—at the expense of a large number of rules, a central point of failure, challenges in ensuring all packets of a session traverse the same middleboxes, and difficulties with middleboxes that modify the “five tuple.” We argue that a session-level protocol is a fundamentally better approach to traffic steering, while naturally supporting host mobility and multihoming in an integrated fashion. In addition, a session-level protocol can enable new capabilities like dynamic service chaining, where the sequence of middleboxes can change during the life of a session, e.g., to remove a load-balancer that is no longer needed, replace a middlebox undergoing maintenance, or add a packet scrubber when traffic looks suspicious. Our Dysco protocol steers the packets of a TCP session through a service chain, and can dynamically reconfigure the chain for an ongoing session. Dysco requires no changes to end-host and middlebox applications, host TCP stacks, or IP routing. Dysco’s distributed reconfiguration protocol handles the removal of proxies that terminate TCP connections, middleboxes that change the size of a byte stream, and concurrent requests to reconfigure different parts of a chain. Through formal verification using Spin and experiments with our prototype, we show that Dysco is provably correct, highly scalable, and able to reconfigure service chains across a range of middleboxes.

The Renaissance of Fasting—Evidence from a Religious Location in Europe

Considered as one of the most religious countries in Europe, Romania draws attention due to a large number of rules and prohibitions in fasting periods in terms of consumption and social behaviour. The study begins with a brief phenomenology of fasting, eventually focusing on Lent, the longest and harshest period of fasting for Romanian Orthodox Christians. The month of March is the only month of the year that usually falls entirely within the fasting period, permitting our analysis of the consumption of animal products and alcohol, products which are to be abstained from. We develop the hypothesis that the growing availability of food over time in Romania is leading to an increasing willingness to fast. Although we find clear evidence of fasting during Lent, the time trend of non-consumption is non-linear but identical for alcohol, animal products and meat. The analysis also shows affluent food supplies in the time after fasting as making fasting more attractive. The qualitative part of the study uses objective hermeneutics to illustrate and complement the statistical analysis. We conclude that the willingness to fast only emerges with a time lag after the availability of food increases, because the experience of oversupply is also an essential ingredient for making fasting an attractive option.

ИСПОЛЬЗОВАНИЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ ПРИ СОСТАВЛЕНИИ ПРОГРАММ РЕМОНТА ЭНЕРГЕТИЧЕСКОГО ОБОРУДОВАНИЯ

The article discusses the process of planning the repair of energy equipment. Using a decision support system is proposed because of the large number of rules of comparing flow charts of technical defects. Such a system can speed up the planning process and reduce economic costs. A conceptual model of the system has been built; further it will be presented as a multi-label classification of cross-cutting classes. The “one-vs-all” approach has been used: each flow chart can use its individual classifier. Metrics are proposed for evaluating classifiers: a portion of accurately classified objects, precision, fullness and F-measure. To summarize the evaluation results the concept of micro-average was chosen. A defect classification algorithm has been described. An experiment was conducted using different classification algorithms: decision trees, Bayes classifier and multilayer perceptron. The results of the experiment proved that 80-90% of the correctly classified objects were found (high values), but the average values of accuracy and fullness occurred low (3-7%). There were found sets of data, where different output data corresponded to similar input data. Thus, machine learning can be used to support decision-making, but in some cases information about the order is not complete. Defect classification can be combined with manual clarifying of results or with different algorithms.

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS. To address these issues, a concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of enhanced soft subspace clustering (ESSC) and sparse learning (SL). In this method, ESSC is used to generate the antecedents and various sparse subspace for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules, based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct con-cise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.

Research on Classification of Tibetan Medical Syndrome in Chronic Atrophic Gastritis

Classification association rules that integrate association rules with classification are playing an important role in data mining. However, the time cost on constructing the classification model, and predicting new instances, will be long, due to the large number of rules generated during the mining of association rules, which also will result in the large system consumption. Therefore, this paper proposed a classification model based on atomic classification association rules, and applied it to construct the classification model of a Tibetan medical syndrome for the common plateau disease called Chronic Atrophic Gastritis. Firstly, introduce the idea of “relative support”, and use the constraint-based Apriori algorithm to mine the strong atomic classification association rules between symptoms and syndrome, and the knowledge base of Tibetan medical clinics will be constructed. Secondly, build the classification model of the Tibetan medical syndrome after pruning and prioritizing rules, and the idea of “partial classification” and “first easy to post difficult” strategy are introduced to realize the prediction of this Tibetan medical syndrome. Finally, validate the effectiveness of the classification model, and compare with the CBA algorithm and four traditional classification algorithms. The experimental results showed that the proposed method can realize the construction and classification of the classification model of the Tibetan medical syndrome in a shorter time, with fewer but more understandable rules, while ensuring a higher accuracy with 92.8%.