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Journal articles on the topic 'Rule extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Grabusts, Peter. "EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 18, 2005): 33. http://dx.doi.org/10.17770/etr2005vol1.2128.

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This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set.

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Liu, Yong, Congfu Xu, Qiong Zhang, and Yunhe Pan. "Rough Rule Extracting From Various Conditions: Incremental and Approximate Approaches for Inconsistent Data." Fundamenta Informaticae 84, no. 3-4 (2008): 403–27. https://doi.org/10.3233/fun-2008-843-408.

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Rough rule extraction refers to the rule induction method by using rough set theory. Although rough set theory is a powerful mathematical tool in dealing with vagueness and uncertainty in data sets, it is lack of effective rule extracting approach under complex conditions. This paper proposes several algorithms to perform rough rule extraction from data sets with different properties. Firstly, in order to obtain uncertainty rules from inconsistent data, we introduce the concept of confidence factor into the rule extracting process. Then, an improved incremental rule extracting algorithm is pro

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Pham, D. T., and M. S. Aksoy. "RULES: A simple rule extraction system." Expert Systems with Applications 8, no. 1 (1995): 59–65. http://dx.doi.org/10.1016/s0957-4174(99)80008-6.

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Geczy, Peter, and Shiro Usui. "Fuzzy Rule Acquisition from Trained Artificial Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 357–67. http://dx.doi.org/10.20965/jaciii.1999.p0357.

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We approach the problem of rule extraction in its primary form. That is, given a trained artificial neural network, we extract rules classifying data set as correctly as possible. Attention is oriented toward extraction of fuzzy rules. The choice of fuzzy rules underlines the aim of balancing rule comprehensibility and complexity. To achieve higher comprehensibility of extracted rules, the formulated theoretical material is an extension of crisp rule extraction 1). A rule extraction algorithm is introduced. The presented algorithm for fuzzy rule extraction implies from the derived theoretical

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MCGARRY, KENNETH, STEFAN WERMTER, and JOHN MACINTYRE. "THE EXTRACTION AND COMPARISON OF KNOWLEDGE FROM LOCAL FUNCTION NETWORKS." International Journal of Computational Intelligence and Applications 01, no. 04 (2001): 369–82. http://dx.doi.org/10.1142/s1469026801000305.

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Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be "shared" across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by extracting rules at two levels: hREX extracts rules by examining the hidden unit to class assignments while mREX extracts rules based on the input space to output space mappings. The rules extracted by our

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Furuhashi, Takeshi. "Rule Extraction from Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 339–40. http://dx.doi.org/10.20965/jaciii.1999.p0339.

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Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This

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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.

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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situ

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S. C. Punitha, S. C. Punitha, Dr P. Ranjit Jeba Thangaiah, and M. Punithavalli M.Punithavalli. "Emulate Rule Extraction From Identical Web Sites Based on Rule Ontology." International Journal of Scientific Research 3, no. 4 (2012): 187–89. http://dx.doi.org/10.15373/22778179/apr2014/64.

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Hayashi, Yoichi, and Naoki Takano. "One-Dimensional Convolutional Neural Networks with Feature Selection for Highly Concise Rule Extraction from Credit Scoring Datasets with Heterogeneous Attributes." Electronics 9, no. 8 (2020): 1318. http://dx.doi.org/10.3390/electronics9081318.

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Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers and rule-extraction methods have not been able to achieve sufficiently high classification accuracies or concise classification rules. This study aims to provide a new approach for achieving transparency and conciseness in credit scoring datasets with heterogeneous attributes by using a one-dimensi

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WATTS, MICHAEL J. "FUZZY RULE EXTRACTION FROM SIMPLE EVOLVING CONNECTIONIST SYSTEMS." International Journal of Computational Intelligence and Applications 04, no. 03 (2004): 299–308. http://dx.doi.org/10.1142/s146902680400132x.

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A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network model is described. The network contains no embedded fuzzy logic elements. The rule extraction algorithm needs no modification of the neural network architecture. No modification of the network learning algorithm is required, nor is it necessary to retain any training examples. The algorithm is illustrated on two well known benchmark data sets and compared with a relevant existing rule extraction algorithm.

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Xing, Yafei, Singo Mabu, Lian Yuzhu, and Kotaro Hirasawa. "Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 5 (2011): 515–24. http://dx.doi.org/10.20965/jaciii.2011.p0515.

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As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by Genetic Network Programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is c

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Lashkia, George, Laurence Anthony, and Hiroyasu Koshimizu. "Classification Rule Extraction Based on Relevant, Irredundant Attributes and Rule Enlargement." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (2007): 389–95. http://dx.doi.org/10.20965/jaciii.2007.p0389.

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In this paper we focus on the induction of classification rules from examples. Conventional algorithms fail in discovering effective knowledge when the database contains irrelevant information. We present a new rule extraction method, RGT, which tackles this problem by employing only relevant and irredundant attributes. Simplicity of rules is also our major concern. In order to create simple rules, we estimate the purity of patterns and propose a rule enlargement approach, which consists of rule merging and rule expanding procedures. In this paper, we describe the methodology for the RGT algor

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Chen, Xinying, Guanyu Li, and Yunhao Sun. "Rule Extraction Model Based on Decision Dependency Degree." Mathematical Problems in Engineering 2019 (November 29, 2019): 1–16. http://dx.doi.org/10.1155/2019/5850410.

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Rule extraction is the core in rough set. Two procedures are contained in rule extraction: one is attribute reduction and another is attribute value reduction. It was proved through computational complexity perspective that obtaining all the reduction, minimum attribute reduction, and minimum attribute value reduction is an NP problem. So, generally, a heuristic reduction method is used to solve attribute reduction and attribute value reduction. However, for most heuristic methods, it is hard to put into practice and has high cost on computational complexity. Moreover, part of the methods extr

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Ishikawa, Masumi. "Rule Extraction by Structural Learning with an Immediate Critic." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 341–47. http://dx.doi.org/10.20965/jaciii.1999.p0341.

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Studies on rule extraction using neural networks have exclusively adopted supervised learning, in which correct outputs are always given as training samples. The real world, however, does not always provide correct answers. We advocate the use of learning with an immediate critic, which is simple reinforcement learning. It uses an immediate binary reinforcement signal indicating whether or not an output is correct. This, of course, makes learning more difficult and time-consuming than supervised learning. Learning with an immediate critic alone, however, is not powerful enough in extracting ru

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Kang, SungKu, Lalit Patil, Arvind Rangarajan, et al. "Automated feedback generation for formal manufacturing rule extraction." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 3 (2019): 289–301. http://dx.doi.org/10.1017/s0890060419000027.

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AbstractManufacturing knowledge is maintained primarily in the unstructured text in industry. To facilitate the reuse of the knowledge, previous efforts have utilized Natural Language Processing (NLP) to classify manufacturing documents or to extract structured knowledge (e.g. ontology) from manufacturing text. On the other hand, extracting more complex knowledge, such as manufacturing rule, has not been feasible in a practical scenario, as standard NLP techniques cannot address the input text that needs validation. Specifically, if the input text contains the information irrelevant to the rul

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Chakraborty, Manomita. "Symbolic Interpretation of Trained Neural Network Ensembles." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 05 (2024): 695–719. http://dx.doi.org/10.1142/s0218488524500168.

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Symbolically representing the knowledge acquired by a neural network is a profound endeavor aimed at illuminating the latent information embedded within the network. The literature offers a multitude of algorithms dedicated to extracting symbolic classification rules from neural networks. While some excel in producing highly accurate rules, others specialize in generating rules that are easily comprehensible. Nevertheless, only a scant few algorithms manage to strike a harmonious balance between comprehensibility and accuracy. One such exemplary technique is the Rule Extraction from Neural Net

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Wang, Chaoqun, Zhongyi Hu, Raymond Chiong, Yukun Bao, and Jiang Wu. "Identification of phishing websites through hyperlink analysis and rule extraction." Electronic Library 38, no. 5/6 (2020): 1073–93. http://dx.doi.org/10.1108/el-01-2020-0016.

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Purpose The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately. Design/methodology/approach Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To co

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Ishibuchi, Hisao, Tadahiko Murata, and Tomoharu Nakashima. "Linguistic Rule Extraction from Numerical Data for High-dimensional Classification Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 386–93. http://dx.doi.org/10.20965/jaciii.1999.p0386.

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We discuss the linguistic rule extraction from numerical data for high-dimensional classification problems. Difficulties in the handling of high-dimensional problems stem from the curse of dimensionality: the number of combinations of antecedent linguistic values exponentially increases as the number of attributes increases. Our goal is to extract a small number of simple linguistic rules with high classification ability. In this paper, the rule extraction is to find a set of linguistic rules using three criteria: its classification ability, its compactness, and the simplicity of each rule. Ou

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Onishi, Soma, Masahiro Nishimura, Ryota Fujimura, and Yoichi Hayashi. "Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm?" Machine Learning and Knowledge Extraction 6, no. 1 (2024): 658–78. http://dx.doi.org/10.3390/make6010031.

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Although machine learning models are widely used in critical domains, their complexity and poor interpretability remain problematic. Decision trees (DTs) and rule-based models are known for their interpretability, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets, even though these approximators often overlook interpretability. These methods generate three types of rule sets: DT based, unordered, and decision list based. However, very few metrics exist that can distinguish and compare these rule sets. Therefore, the present study proposes

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Zhang, Xian Yong, and Duo Qian Miao. "LBRM Algorithm for Rule Extraction Based on Rough Membership." Advanced Materials Research 791-793 (September 2013): 1088–91. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1088.

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Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough membership. Thus, LBRM Algorithm is proposed by utilizing discretization and clearness strategies under the fuzzy environment, and is applied to both interval rules and general rules in fuzzy classification. LBRM Algorithm effectiveness is illustrated by a medical example. In particular, LBRM Algorithm integrates the confidence on both previous LBR Algorithm and fundamental rough

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Bologna, Guido. "A Rule Extraction Technique Applied to Ensembles of Neural Networks, Random Forests, and Gradient-Boosted Trees." Algorithms 14, no. 12 (2021): 339. http://dx.doi.org/10.3390/a14120339.

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In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation. A natural way to explain the classifications of the models is to transform them into propositional rules. In this work, we focus on random forests and gradient-boosted trees. Specifically, these models are converted into an ensemble of interpretable MLPs from which propositional rules are produced. The rule extraction method presented here allows

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Huang, Min Chao, and Bao Yu Xing. "Fault Diagnosis Simulation of a Space Propulsion System Based on Fuzzy Rule Set Neural Network Method." Applied Mechanics and Materials 727-728 (January 2015): 876–79. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.876.

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Based on fuzzy rule sets match method which is a series of fuzzy neural networks, a system framework used for the fault diagnosis is proposed. This fault diagnosis system consists of five parts, including the extraction of fuzzy rules, fuzzy reference rule sets, the fuzzy rule scheduled to detect, the fuzzy match module and the diagnosis logic module. The extraction of fuzzy rules involves two steps: step one adaptively divides the whole space of the trained data into the subspaces in the form of hypersphere, which is expected efficiently to work out the recognition questions in the high dimen

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Das, Madhabananda, Rahul Roy, Satchidananda Dehuri, and Sung-Bae Cho. "A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization." International Journal of Applied Metaheuristic Computing 2, no. 2 (2011): 51–73. http://dx.doi.org/10.4018/jamc.2011040103.

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Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multi-objective problem rather than a single objective one. As the problem is a discrete combinatorial optimization problem, it was necessary to develop a binary multi-objective particle swarm optimization (BMOPSO) to optimize the measure like coverage and confidence of association rule mining (ARM)

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Duch, Wlodzislaw, Rafal Adamczak, KrzysAof Grabczewski, and Grzegorz Zal. "Hybrid Neural-global Minimization Method of Logical Rule Extraction." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 348–56. http://dx.doi.org/10.20965/jaciii.1999.p0348.

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Methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multilayered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing logical functions. A constructive algorithm called CMLP2LN is proposed, in which rules of increasing specificity are generated consecutively by adding more nodes to the network. Neural rule extraction is followed by optimiz

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Wang, Dian Gang, Jia Shi Yang, Ya Qi Ni, Ruo Fan Liu, Jun Yong Liu, and Hui Gong. "A Scenario Clustering Method for Extracting TTC Operation Rules of Transmission Corridors with Wind Power Integration." Applied Mechanics and Materials 543-547 (March 2014): 528–32. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.528.

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.Based on the formulation of conventional operational rule, a scenario clustering method for extracting TTC operational rules of transmission corridors with the integration of wind power is presented in more detail. The scenario clustering for the network state is obtained on the basis of the output-load level of wind farm; in a certain scenario, the rules can be utilized to provide decision support for the dispatchers. The formation of the rule is proposed by using the data mining techniques, consisting of sample generation, feature selection and rule extraction. IEEE-RTS79 system simulation

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GopiKrishna, Tiruveedula. "Evaluation of Rule Extraction Algorithms." International Journal of Data Mining & Knowledge Management Process 4, no. 3 (2014): 9–19. http://dx.doi.org/10.5121/ijdkp.2014.4302.

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Ching-Chang Wong and Nine-Shen Lin. "Rule extraction for fuzzy modeling." Fuzzy Sets and Systems 88, no. 1 (1997): 23–30. http://dx.doi.org/10.1016/s0165-0114(96)00054-1.

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Ishikawa, M. "Rule extraction by successive regularization." Neural Networks 13, no. 10 (2000): 1171–83. http://dx.doi.org/10.1016/s0893-6080(00)00072-1.

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Bologna, Guido. "A Simple Convolutional Neural Network with Rule Extraction." Applied Sciences 9, no. 12 (2019): 2411. http://dx.doi.org/10.3390/app9122411.

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Classification responses provided by Multi Layer Perceptrons (MLPs) can be explained by means of propositional rules. So far, many rule extraction techniques have been proposed for shallow MLPs, but not for Convolutional Neural Networks (CNNs). To fill this gap, this work presents a new rule extraction method applied to a typical CNN architecture used in Sentiment Analysis (SA). We focus on the textual data on which the CNN is trained with “tweets” of movie reviews. Its architecture includes an input layer representing words by “word embeddings”, a convolutional layer, a max-pooling layer, fol

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Guo, Hui Ling. "Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection." Advanced Materials Research 760-762 (September 2013): 857–61. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.857.

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It is necessary to establish the rule base before intrusion detection. An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules in order to realize the automation of attack rule generation. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment.

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Shen, Jin, Bin Wu, and Li Yu. "Personalized configuration rules extraction in product service systems by using Local Cluster Neural Network." Industrial Management & Data Systems 115, no. 8 (2015): 1529–46. http://dx.doi.org/10.1108/imds-03-2015-0092.

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Purpose – Configuration systems are used as a means for efficient design of customer tailored product service systems (PSS). In PSS configuration, mapping customer needs with optimal configuration of PSS components have become much more challenging, because more knowledge with personalization aspects has to be considered. However, the extant techniques are hard to be applied to acquire personalized configuration rules. The purpose of this paper is to extract the configuration rule knowledge in symbolism formulation from historical data. Design/methodology/approach – Customer characteristics (C

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Yin Lv, Yin Lv. "An Intelligent Cost Review Scheme for Power Engineering Projects Based on Natural Language Processing." Journal of Software Engineering and Simulation 11, no. 1 (2025): 30–40. https://doi.org/10.35629/3795-11013040.

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Traditional manual cost review in power engineering projects faces inefficiencies and inaccuracies due to the complexity of data and rules. This study aims to introduce an intelligent cost review scheme that automates data extraction and rule interpretation using natural language processing techniques and completes automatic review. The proposed scheme consists of three modules: information extraction, rule interpretation, and intelligent review, utilizing techniques such as named entity recognition and relation extraction. Experimental results demonstrate the scheme's effectiveness in accurat

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Chatvichienchai, Somchai. "SEMEXSS — A Rule-Based Semantic Metadata Extraction System for Spreadsheets." International Journal of Computer Theory and Engineering 8, no. 2 (2016): 102–8. http://dx.doi.org/10.7763/ijcte.2016.v8.1027.

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Ladas, Nektarios, Florian Borchert, Stefan Franz, et al. "Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts." Health Informatics Journal 29, no. 2 (2023): 146045822311646. http://dx.doi.org/10.1177/14604582231164696.

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Background Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. Objectives In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is

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Gildea, Daniel, Giorgio Satta, and Xiaochang Peng. "Ordered Tree Decomposition for HRG Rule Extraction." Computational Linguistics 45, no. 2 (2019): 339–79. http://dx.doi.org/10.1162/coli_a_00350.

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We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure. The assumption of a fixed order for the vertices of the input graph makes it possible to solve the problem in polynomial time, in contrast to the fact that the problem of finding optimal tree decompositions for a graph is NP-hard. We also present polynomial-time algorithms for parsing based on our HRGs, where th

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D’ALCHÉ-BUC, FLORENCE, VINCENT ANDRÈS, and JEAN-PIERRE NADAL. "RULE EXTRACTION WITH FUZZY NEURAL NETWORK." International Journal of Neural Systems 05, no. 01 (1994): 1–11. http://dx.doi.org/10.1142/s0129065794000025.

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This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a dis

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Hayashi, Yoichi. "Use of a Deep Belief Network for Small High-Level Abstraction Data Sets Using Artificial Intelligence with Rule Extraction." Neural Computation 30, no. 12 (2018): 3309–26. http://dx.doi.org/10.1162/neco_a_01139.

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We describe a simple method to transfer from weights in deep neural networks (NNs) trained by a deep belief network (DBN) to weights in a backpropagation NN (BPNN) in the recursive-rule eXtraction (Re-RX) algorithm with J48graft (Re-RX with J48graft) and propose a new method to extract accurate and interpretable classification rules for rating category data sets. We apply this method to the Wisconsin Breast Cancer Data Set (WBCD), the Mammographic Mass Data Set, and the Dermatology Dataset, which are small, high-abstraction data sets with prior knowledge. After training these three data sets,

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Khatriker, S., and M. Kumar. "BUILDING FOOTPRINT EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY USING SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 123–28. http://dx.doi.org/10.5194/isprs-archives-xlii-5-123-2018.

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<p><strong>Abstract.</strong> Identification and mapping of urban features such as buildings and roads are an important task for cartographers and urban planners. High resolution satellite imagery supports the efficient extraction of manmade objects. For the planning and designing of Smart cities, building footprint information is an essential component, and geospatial technologies helps in creating this large mass of data inputs for designing and planning of smart cities. In this study segmentation approach is followed for building extraction. For extraction of buildings esp

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Zhou, Huiyu, Wei Wei, Kaoru Shimada, Shingo Mabu, and Kotaro Hirasawa. "Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 467–78. http://dx.doi.org/10.20965/jaciii.2008.p0467.

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In this paper, we propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. GNP, a kind of evolutionary computation, represents solutions using graph structures. Because of the inherent features of GNP, it works well in dynamic environments. In this paper, GNP is applied to generate candidate association rules using the database consisting of a large number of time related attributes. In order to d

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Biswas, Saroj Kumar, Manomita Chakraborty, Biswajit Purkayastha, Pinki Roy, and Dalton Meitei Thounaojam. "Rule Extraction from Training Data Using Neural Network." International Journal on Artificial Intelligence Tools 26, no. 03 (2016): 1750006. http://dx.doi.org/10.1142/s0218213017500063.

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Data Mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. One of the most commonly used techniques in data mining is Artificial Neural Network due to its high performance in many application domains. Despite many advantages of Artificial Neural Network, one of its main drawbacks is its inherent black box nature which is the main problem of using Artificial Neural Network in data mining. Therefore, this paper proposes a rule extraction algorithm from neural network using classified and misclassified data

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Lu, Yilin, Xiaoqiang Wang, Haofeng Yang, and Siliang Tang. "KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13336–43. http://dx.doi.org/10.1609/aaai.v37i11.26565.

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Machine Learning is often challenged by insufficient labeled data. Previous methods employing implicit commonsense knowledge of pre-trained language models (PLMs) or pattern-based symbolic knowledge have achieved great success in mitigating manual annotation efforts. In this paper, we focus on the collaboration among different knowledge sources and present KICE, a Knowledge-evolving framework by Iterative Consolidation and Expansion with the guidance of PLMs and rule-based patterns. Specifically, starting with limited labeled data as seeds, KICE first builds a Rule Generator by prompt-tuning t

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Holeňa, Martin. "Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data." Neural Computation 18, no. 11 (2006): 2813–53. http://dx.doi.org/10.1162/neco.2006.18.11.2813.

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This article addresses the topic of extracting logical rules from data by means of artificial neural networks. The approach based on piecewise linear neural networks is revisited, which has already been used for the extraction of Boolean rules in the past, and it is shown that this approach can be important also for the extraction of fuzzy rules. Two important theoretical properties of piecewise-linear neural networks are proved, allowing an elaboration of the basic ideas of the approach into several variants of an algorithm for the extraction of Boolean rules. That algorithm has already been

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Watanabe, Toshihiko. "An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1248–55. http://dx.doi.org/10.20965/jaciii.2011.p1248.

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In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm bas

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Wu, Bin, and Liu Bo Ouyang. "A Method of Domain Compound Concept Extraction Based on Multilevel Filter." Advanced Materials Research 989-994 (July 2014): 2292–96. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2292.

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Identification and extraction in domain compound concept is the basis of the domain text information processing. This paper builds a multilevel filter extraction model by fusing the thought of statistics and language rule. Firstly, the extraction model screening out domain atomic concept set by using method of improved TF-IDF. We secondly build a space combination rule, screening out initial domain compound concept set. Ultimately we screening out finally domain compound concept set by using POS rules template matching via POS analysis. Experiments show that this method can effectively identif

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Setiowati, Yuliana, Fitri Setyorini, and Afrida Helen. "Penentuan Aspek Implisit dengan Ekstraksi Knowledge Berbasis Rule pada Ulasan Bahasa Indonesia (Determination of Implicit Aspects with Rule Based Knowledge Extraction in Indonesian Reviews)." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 1 (2020): 35–44. http://dx.doi.org/10.22146/jnteti.v9i1.145.

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Determination of implicit aspects is one of the important things in opinion sentences. This study proposes a new approach for developing rule-based knowledge by forming a relation between opinion words with aspect categories. The relationship is obtained from the combination of rules, based on Opinion Word Similarity (OWS). Evaluation for rule-based knowledge extraction is in the form of threshold values of frequency and confidence to produce the best precision, recall, and f-measure values. The knowledge extraction consists of two phases: training phase and testing phase. The training phase i

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Huysmans, J., R. Setiono, B. Baesens, and J. Vanthienen. "Minerva: Sequential Covering for Rule Extraction." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 2 (2008): 299–309. http://dx.doi.org/10.1109/tsmcb.2007.912079.

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Liu, Bin, Laura Chiticariu, Vivian Chu, H. V. Jagadish, and Frederick R. Reiss. "Automatic rule refinement for information extraction." Proceedings of the VLDB Endowment 3, no. 1-2 (2010): 588–97. http://dx.doi.org/10.14778/1920841.1920916.

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Setiono, Rudy, James Y. L. Thong, and Chee-Sing Yap. "Symbolic rule extraction from neural networks." Information & Management 34, no. 2 (1998): 91–101. http://dx.doi.org/10.1016/s0378-7206(98)00048-2.

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Maire, F. "Rule-extraction by backpropagation of polyhedra." Neural Networks 12, no. 4-5 (1999): 717–25. http://dx.doi.org/10.1016/s0893-6080(99)00013-1.

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Setnes, M. "Supervised fuzzy clustering for rule extraction." IEEE Transactions on Fuzzy Systems 8, no. 4 (2000): 416–24. http://dx.doi.org/10.1109/91.868948.

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