Relevant bibliographies by topics / Classification rule

Academic literature on the topic 'Classification rule'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Classification rule"

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Zhou, Zhongmei. "A New Classification Approach Based on Multiple Classification Rules." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/818253.

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A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when the minimum support is set to be low. It is difficult to select a high quality rule set for classification. Second, the accuracy of associative classification depends on the setting of the minimum support and the minimum confidence. In comparison with associative classification, some improved traditional rule-based classification approaches often produce a classification rule set that plays an important role in prediction. Thus, some improved traditional rule-based classification approaches not only achieve better efficiency than associative classification but also get higher accuracy. In this paper, we put forward a new classification approach called CMR (classification based on multiple classification rules). CMR combines the advantages of both associative classification and rule-based classification. Our experimental results show that CMR gets higher accuracy than some traditional rule-based classification methods.
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BEAUSOLEIL, RICARDO P. "ASSOCIATIVE CLASSIFICATION WITH MULTIOBJECTIVE TABU SEARCH." Revista de Matemática: Teoría y Aplicaciones 27, no. 2 (June 23, 2020): 353–74. http://dx.doi.org/10.15517/rmta.v27i2.42438.

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This paper presents an application of Tabu Search algorithm to association rule mining. We focus our attention specifically on classification rule mining, often called associative classification, where the consequent part of each rule is a class label. Our approach is based on seek a rule set handled as an individual. A Tabu search algorithm is used to search for Pareto-optimal rule sets with respect to some evaluation criteria such as accuracy and complexity. We apply a called Apriori algorithm for an association rules mining and then a multiobjective tabu search to a selection rules. We report experimental results where the effect of our multiobjective selection rules is examined for some well-known benchmark data sets from the UCI machine learning repository.
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Hasanpour, Hesam, Ramak Ghavamizadeh Meibodi, and Keivan Navi. "Improving rule-based classification using Harmony Search." PeerJ Computer Science 5 (November 18, 2019): e188. http://dx.doi.org/10.7717/peerj-cs.188.

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Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.
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Thabtah, Fadi. "Rule Preference Effect in Associative Classification Mining." Journal of Information & Knowledge Management 05, no. 01 (March 2006): 13–20. http://dx.doi.org/10.1142/s0219649206001281.

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Classification based on association rule mining, also known as associative classification, is a promising approach in data mining that builds accurate classifiers. In this paper, a rule ranking process within the associative classification approach is investigated. Specifically, two common rule ranking methods in associative classification are compared with reference to their impact on accuracy. We also propose a new rule ranking procedure that adds more tie breaking conditions to the existing methods in order to reduce rule random selection. In particular, our method looks at the class distribution frequency associated with the tied rules and favours those that are associated with the majority class. We compare the impact of the proposed rule ranking method and two other methods presented in associative classification against 14 highly dense classification data sets. Our results indicate the effectiveness of the proposed rule ranking method on the quality of the resulting classifiers for the majority of the benchmark problems, which we consider. This provides evidence that adding more appropriate constraints to break ties between rules positively affects the predictive power of the resulting associative classifiers.
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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 (April 20, 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 algorithm, discuss its properties, and compare it with conventional methods.
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Baralis, Elena, and Silvia Chiusano. "Essential classification rule sets." ACM Transactions on Database Systems 29, no. 4 (December 12, 2004): 635–74. http://dx.doi.org/10.1145/1042046.1042048.

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SUN, JIGUI, HUAWEN LIU, and CHANGSONG QI. "A MULTISTAGE RULE INDUCTION ALGORITHM IN CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (June 2007): 693–708. http://dx.doi.org/10.1142/s0218001407005624.

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The purpose of this paper is to start a conceptual investigation of approximation rule based on VPRS as a result of the certainty degree of rules in complete information system that cannot exactly express the uncertainty of those in incomplete information system, and then an efficient approximation rule induction algorithm under the rough set framework is presented. Instead of focusing on the minimal rule set, this algorithm hierarchically extracts rules in multistages from data sets to suit changing environments in learning and classification. In addition, a heuristic strategy is employed in the algorithm to improve its performance and reduce the time consumed in inducing. Experiments are carried out, and the results show that the proposed algorithm is effective in inducing rules which can enhance their adaptive capacities.
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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 (April 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) to extract classification rules in rule extraction phase. In rule selection phase, a small number of rules are targeted from the extracted rules by BMOPSO to design an accurate and compact classifier which can maximize the accuracy of the rule sets and minimize their complexity simultaneously. Experiments are conducted on some of the University of California, Irvine (UCI) repository datasets. The comparative result of the proposed method with other standard classifiers confirms that the new proposed approach can be a suitable method for classification.
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Thanajiranthorn, Chartwut, and Panida Songram. "Efficient Rule Generation for Associative Classification." Algorithms 13, no. 11 (November 17, 2020): 299. http://dx.doi.org/10.3390/a13110299.

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Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).
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Karyawati, Eka, and Edi Winarko. "Class Association Rule Pada Metode Associative Classification." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (November 19, 2011): 17. http://dx.doi.org/10.22146/ijccs.5207.

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Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining. Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list Intersection algorithm. This paper focuses on surveying and comparing the state of the art associative classification techniques with regards to the rule generation phase of associative classification algorithms. This phase includes frequent itemsets discovery and rules mining/extracting methods to generate the set of class association rules (CARs). There are some techniques proposed to improve the rule generation method. A technique by utilizing the concepts of discriminative power of itemsets can reduce the size of frequent itemset. It can prune the useless frequent itemsets. The closed frequent itemset concept can be utilized to compress the rules to be compact rules. This technique may reduce the size of generated rules. Other technique is in determining the support threshold value of the itemset. Specifying not single but multiple support threshold values with regard to the class label frequencies can give more appropriate support threshold value. This technique may generate more accurate rules. Alternative technique to generate rule is utilizing the vertical layout to represent dataset. This method is very effective because it only needs one scan over dataset, compare with other techniques that need multiple scan over dataset. However, one problem with these approaches is that the initial set of tid-lists may be too large to fit into main memory. It requires more sophisticated techniques to compress the tid-lists.
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Dissertations / Theses on the topic "Classification rule"

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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Strandberg, von Schantz Mathilda. "Rule-based classification of heavy vehicle operations." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254983.

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The problem explored in this thesis is a supervised classification problem. Input data consists of operational and manufacturing data of a truck. The output denotes its operation, i.e. its basic utility and usage pattern, such as “Long distance” or “On and off-road”. In order to understand the distinction between the operation categories in practice, we look at interpretable classifiers. The examined classifiers are treeand rule-based classifiers, as they are the most interpretable. These include random forest, decision tree, and a classifier called inTrees, a method that summarizes a random forest using rules. In addition, a suggested method is examined. The suggested method works similarly to inTrees, but differs in the rule selection step. The question is whether this suggested method is better than inTrees in terms of interpretability, and how well both of them perform in comparison to a decision tree and a random forest. Another question regards the operation category of trucks, and whether they can be successfully distinguished using these methods.In order to compare the methods, their balanced accuracy, number of rules and other measures are recorded for the truck data set and additional data sets. Additional data sets are used to get a more exhaustive comparison between the methods.The suggested method does not outperform inTrees, and frequently uses three to four times as many rules to achieve the same accuracy on a given data set. Results indicate that the suggested method could perform more similarly to inTrees, given a different form of hyperparameter tuning. Additionally, it is shown that using interpretable classifiers rather than a random forest means we use less than one percent of the rules, at the cost of a loss of 10 percentage points in balanced accuracy.
Problemet som utforskas i detta examensarbete är ett problem inom övervakat lärande där indata består av driftdata samt tillverkningsspecifikationer för en lastbil, och utdata är dess användningsområde, såsom “Långdistans” eller “Stadsdistribution”. Målet är att få insikt i vad distinktionen mellan lastbilars användningsområden är i praktiken. För att utreda detta används regeloch trädbaserade klassificerare. Dessa används eftersom de är de mest tolkningsbara klassificerarna. De klassificerare som ingår är random forest, beslutsträd och en klassificerare kallad inTrees, som extraherar regler från en random forest. Utöver detta föreslås en ny metod som bygger på inTrees, men som skiljer sig i hur den väljer regler.Frågeställningen är om den föreslagna metoden ger resultat av högre tolkningsbarhet än inTrees, och hur väl bägge presterar i jämförelse med ett beslutsträd och en random forest. En annan del av frågeställningen gäller vad för slutsatser som kan dras kring användningsområde av lastbilar.För att jämföra prestandan av dessa metoder undersöktes både prediktionsgraden och tolkningsbarheten. Detta gjordes för lastbilsdatat men även andra publika dataset. Andra dataset användes för att få en mer omfattande jämförelse.Den föreslagna metoden är mindre tolkningsbar än inTrees då den ofta kräver tre till fyra gånger så många regler för att uppnå samma precision för ett dataset. Vissa resultat indikerar att den föreslagna metoden kunnat prestera mer likt inTrees om en annan hyperparameter-optimisering hade använts. Ytterligare resultat visade att vi, genom att använda tolkningsbara klassificerare istället för random forest, förlorade 10 procentenheter i balanserad precision men använde mindre än en procent av reglerna.
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Janidlo, Peter S. "Rule-based expert systems and tonal chord classification." Virtual Press, 1999. http://liblink.bsu.edu/uhtbin/catkey/1137841.

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The purpose of the proposed thesis is to:1. Define expert systems and discuss various implementation techniques for the components of expert systems. This includes discussion on knowledge representation, inference methods, methods for dealing with uncertainty, and methods of explanation. Specifically, the focus will be on the implementation of rule-based expert systems;2. Apply selected expert system techniques to a case study. The case study will be a rule-based expert system in Prolog to recognize and identify musical chords from tonal harmony. The system will have a general knowledge base containing fundamental rules about chord construction. It will also contain some knowledge that will allow it to deduce non-trivial chords. Furthermore, it will contain procedures to deal with uncertainty and explanation;3. Explain general concepts about music theory and tonal chord classification to put the case study in context; and4. Discuss the limitations of expert systems based on the results of the case study and the current literature.
Department of Computer Science
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GONG, RONGSHENG. "A KNOWLEDGE-BASED MODELING TOOL FOR CLASSIFICATION." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1153746991.

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Mahmood, Qazafi. "LC - an effective classification based association rule mining algorithm." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24274/.

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Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider.
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Dan, Qing. "A fuzzy rule-based approach for edge feature classification." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ39646.pdf.

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Liu, Han. "Rule based systems for classification in machine learning context." Thesis, University of Portsmouth, 2015. https://researchportal.port.ac.uk/portal/en/theses/rule-based-systems-for-classification-in-machine-learning-context(1790225c-ceb1-48d3-9e05-689edbfa3ef1).html.

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This thesis introduces a unified framework for design of rule based systems for classification tasks, which consists of the operations of rule generation, rule simplification and rule representation. This thesis also stresses the importance of combination of different rule learning algorithms through ensemble learning approaches. For the three operations mentioned above, novel approaches are developed and validated by comparing with existing ones for advancing the performance of using this framework. In particular, for rule generation, Information Entropy Based Rule Generation is developed and validated through comparing with Prism. For rule simplification, Jmid-pruning is developed and validated through comparing with J-pruning and Jmax-pruning. For rule representation, rule based network is developed and validated through comparing with decision tree and linear list. The results show that the novel approaches complement well the existing ones in terms of accuracy, efficiency and interpretability. On the other hand, this thesis introduces ensemble learning approaches that involve collaborations in training or testing stage through combination of learning algorithms or models. In particular, the novel framework Collaborative and Competitive Random Decision Rules is created and validated through comparing with Random Prisms. This thesis also introduces the other novel framework Collaborative Rule Generation which involves collaborations in training stage through combination of multiple learning algorithms. This framework is validated through comparing with each individual algorithm. In addition, this thesis shows that the above two frameworks can be combined as a hybrid ensemble learning framework toward advancing overall performance of classification. This hybrid framework is validated through comparing with Random Forests. Finally, this thesis summarises the research contributions in terms of theoretical significance, practical importance, methodological impact and philosophical aspects. In particular, theoretical significance includes creation of the framework for design of rule based systems and development of novel approaches relating to rule based classification. Practical importance shows the usefulness in knowledge discovery and predictive modelling and the independency in application domains and platforms. Methodological impact shows the advances in generation, simplification and representation of rules. Philosophical aspects include the novel understanding of data mining and machine learning in the context of human research and learning, and the inspiration from information theory, system theory and control theory toward methodological innovations. On the basis of the completed work, this thesis provides suggestions regarding further directions toward advancing this research area.
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Yoshioka, Atsushi. "Rule hashing for efficient packet classification in network intrusion detection." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Thesis/Fall2007/a_yoshioka_120307.pdf.

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Soltan-Zadeh, Yasaman. "Improved rule-based document representation and classification using genetic programming." Thesis, Royal Holloway, University of London, 2011. http://repository.royalholloway.ac.uk/items/479a1773-779b-8b24-b334-7ed485311abe/8/.

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Hammoud, Suhel. "MapReduce network enabled algorithms for classification based on association rules." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5833.

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There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.
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Books on the topic "Classification rule"

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Dass, Rajanish. Classification using association rules. Ahmedabad: Indian Institute of Management, 2008.

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Cassidy, Bonnie Bisol. Ambulatory payment classification: Summary of the final rule. Gaithersburg, Md: Aspen Pub., 2000.

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Schoenfeld, Gabriel. Necessary secrets: National security, the media, and the rule of law. New York: W. W. Norton & Co., 2010.

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Necessary secrets: National security, the media, and the rule of law. New York: W. W. Norton & Co., 2010.

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Taylor, Arlene G. Introduction to cataloging and classification. Westport, Conn: Libraries Unlimited, 2006.

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Lloyd's Register of Shipping (Firm : 1914- ). Classification of ships: Rules and regulations. London: Lloyd's Register of Shipping, 1998.

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Construction and assessment of classification rules. Chichester: Wiley, 1997.

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Lloyd's Register of Shipping (Firm : 1914- ). Classification of ships: Rules and regulations. London: Lloyd's Register of Shipping, 1997.

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Library of Congress. Cataloger's desktop: Classification plus. Washington, D.C: Library of Congress, Cataloging Distribution Service, 1999.

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Library of Congress. Cataloger's desktop: Classification plus. Washington, D.C: Library of Congress, Cataloging Distribution Service, 1999.

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Book chapters on the topic "Classification rule"

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Abe, Shigeo. "Fuzzy Rule Generation." In Pattern Classification, 263–86. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_15.

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Fürnkranz, Johannes. "Classification Rule." In Encyclopedia of Machine Learning and Data Mining, 1. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_914-1.

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Fürnkranz, Johannes. "Classification Rule." In Encyclopedia of Machine Learning and Data Mining, 209. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_914.

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Abe, Shigeo. "Static Fuzzy Rule Generation." In Pattern Classification, 81–107. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_5.

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Abe, Shigeo. "Dynamic Fuzzy Rule Generation." In Pattern Classification, 177–96. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_9.

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Abe, Shigeo. "Fuzzy Rule Representation and Inference." In Pattern Classification, 257–61. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_14.

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Tung, Anthony K. H. "Rule-Based Classification." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_559-2.

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Tung, Anthony K. H. "Rule-based Classification." In Encyclopedia of Database Systems, 2459–62. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_559.

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Tung, Anthony K. H. "Rule-Based Classification." In Encyclopedia of Database Systems, 3265–68. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_559.

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Wilhelm, Adalbert F. X., Arne Jacobs, and Thorsten Hermes. "Association Rule Mining of Multimedia Content." In Data Analysis and Classification, 179–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_21.

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Conference papers on the topic "Classification rule"

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Horte, T., R. Skjong, P. Friis-Hansen, A. P. Teixeira, and F. Viejo de Francisco. "Probabilistic Methods Applied To Structural Design And Rule Development." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.07.

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Lindgren, Tony. "Indexing rules in rule sets for fast classification." In the International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2952744.2952750.

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"INDUCING COOPERATION IN FUZZY CLASSIFICATION RULES USING ITERATIVE RULE LEARNING AND RULE-WEIGHTING." In 6th International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002209900620067.

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Guan, Yu. "Regularization Method for Rule Reduction in Belief Rule-based SystemRegularization Method for Rule Reduction in Belief Rule-based System." In 8th International Conference on Computational Science and Engineering (CSE 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101705.

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Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.
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El Bendadi, Khawla, Yissam Lakhdar, and El Hassan Sbai. "Kernel credal classification rule." In Ninth International Conference on Machine Vision, edited by Antanas Verikas, Petia Radeva, Dmitry P. Nikolaev, Wei Zhang, and Jianhong Zhou. SPIE, 2017. http://dx.doi.org/10.1117/12.2268730.

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Zhang, Te, Christian Wagner, and Jonathan M. Garibaldi. "Counterfactual rule generation for fuzzy rule-based classification systems." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882705.

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Kumar, M. Naresh, and B. Eswara Reddy. "Improved classification association rule mining." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228045.

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Guinde, Nitesh B., Roberto Rojas-Cessa, and Sotirios G. Ziavras. "Packet classification using rule caching." In 2013 Fourth International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2013. http://dx.doi.org/10.1109/iisa.2013.6623734.

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Janusz, Andrzej. "Rule-Based Similarity for Classification." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.323.

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Diao, Hongyue, Ansheng Deng, Wei Yan, and Li Zou. "Rule Inference Network for Classification." In 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2019. http://dx.doi.org/10.1109/iske47853.2019.9170429.

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Reports on the topic "Classification rule"

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Zeitouni, Ofer, and Sanjeev R. Kulkarni. A General Classification Rule for Probability Measures. Fort Belvoir, VA: Defense Technical Information Center, August 1993. http://dx.doi.org/10.21236/ada455893.

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SCHelchkov, K. A. Classification of General Binding Rules. OFERNIO, January 2023. http://dx.doi.org/10.12731/ofernio.2023.25094.

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Levi, Brittany E. Choledochal Cysts: In Brief with Dr. Alexander Bondoc. Stay Current, May 2022. http://dx.doi.org/10.47465/sc1.

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Choledochal cysts are a core pathology in pediatric surgery, affecting 1/100,000 live births in the western world, and 1/13,000 in eastern asia. These cysts are classified by the Todani classification, types I-V, in respect to their location and underlying pathophysiology. Infants and children presenting with stigmata of biliary disease should undergo evaluation for choledocal cyst. Workup includes axial imaging, ultrasonography, and laboratory investigation. A liver biopsy is necessary in neonates and newborns to rule out cystic biliary atresia, which would require further evaluation and management. Large choledochal cysts may be visualized on prenatal ultrasound, and warrant referral to a fetal care center for postnatal work up and monitoring. Management of choledochal cysts is dependent on the anatomic variant and spans from ERCP with sphincterotomy, to cyst excision with ductal and alimentary tract reconstruction. Type V choledochal cysts may require liver transplantation. Long term follow up is required due to an enhanced risk of future malignancy, even after resection.
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Levi, Brittany E., Rodrigo G. Gerardo, Alexander J. Bondoc, Rachel E. Hanke, Chandler Gibson, Ellen M. Encisco, and Todd A. Ponsky. Choledochal Cysts: In Brief with Dr. Alexander Bondoc. Stay Current, May 2022. http://dx.doi.org/10.47465/sc00001.

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Choledochal cysts are a core pathology in pediatric surgery, affecting 1/100,000 live births in the western world, and 1/13,000 in eastern asia. These cysts are classified by the Todani classification, types I-V, in respect to their location and underlying pathophysiology. Infants and children presenting with stigmata of biliary disease should undergo evaluation for choledocal cyst. Workup includes axial imaging, ultrasonography, and laboratory investigation. A liver biopsy is necessary in neonates and newborns to rule out cystic biliary atresia, which would require further evaluation and management. Large choledochal cysts may be visualized on prenatal ultrasound, and warrant referral to a fetal care center for postnatal work up and monitoring. Management of choledochal cysts is dependent on the anatomic variant and spans from ERCP with sphincterotomy, to cyst excision with ductal and alimentary tract reconstruction. Type V choledochal cysts may require liver transplantation. Long term follow up is required due to an enhanced risk of future malignancy, even after resection.
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DeJong, Kenneth A., and William M. Spears. Learning Concept Classification Rules using Genetic Algorithms,. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada294470.

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Wang, Jianyong, and George Karypis. HARMONY: Efficiently Mining the Best Rules for Classification. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada439469.

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Stark, Timothy, Abedalqader Idries, Lucia Moya, and Abdolrzea Osouli. Beneficial Use of Dredged Material from the Illinois Marine Transportation System. Illinois Center for Transportation, November 2022. http://dx.doi.org/10.36501/0197-9191/22-022.

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This project presents several successful case studies in 15 categories of dredged material along with the statutory and regulatory requirements for beneficial use of dredged material in Illinois. The Illinois Environmental Protection Agency classification criteria for contaminated and uncontaminated dredged material are included with emphasis on Illinois requirements and characterization. Nine sites that have sandy dredged material stockpiles in Illinois are presented with suggestions for beneficially using the material. Based on this study, there is a high potential for beneficially using dredged material in Illinois for a range of projects. Currently, it is a state policy in Illinois to formally evaluate the history of possible nearby sources of chemicals that may have impacted the project sediments and to test the dredged material for chemical contamination before accepting for use on any highway project. However, the research team suggest that if the dredged material is mainly uncontaminated sand (e.g., greater than 80% sand) and is from a local site that does not have a history of contamination as determined by a formal evaluation, then the material is unlikely to be contaminated and may be easier to use and require little to no contaminate testing. Nevertheless, this proposed rule needs more testing and examination to be verified.
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Lyness, J., and W. Newman. A classification of lattice rules using the reciprocal lattice generator matrix. Office of Scientific and Technical Information (OSTI), June 1989. http://dx.doi.org/10.2172/5886138.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Ruiz, Pablo, Craig Perry, Alejando Garcia, Magali Guichardot, Michael Foguer, Joseph Ingram, Michelle Prats, Carlos Pulido, Robert Shamblin, and Kevin Whelan. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, November 2020. http://dx.doi.org/10.36967/nrr-2279586.

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The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within these two federally-managed units as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in park management, resource management, research, and monitoring. This mapping project covers an area of approximately 7,400 square kilometers (1.84 million acres [ac]) and consists of seven mapping regions: four regions in Everglades National Park, Regions 1–4, and three in Big Cypress National Preserve, Regions 5–7. The report focuses on the mapping effort associated with the Northwest Coastal Everglades (NWCE), Region 4 , in Everglades National Park. The NWCE encompasses a total area of 1,278 square kilometers (493.7 square miles [sq mi], or 315,955 ac) and is geographically located to the south of Big Cypress National Preserve, west of Shark River Slough (Region 1), and north of the Southwest Coastal Everglades (Region 3). Photo-interpretation was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed specifically for this project. Additionally, photo-interpreters categorized the absolute cover of cattail (Typha sp.) and any invasive species detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%). A total of 178 thematic classes were used to map the NWCE. The most common vegetation classes are Mixed Mangrove Forest-Mixed and Transitional Bayhead Shrubland. These two communities accounted for about 10%, each, of the mapping area. Other notable classes include Short Sawgrass Marsh-Dense (8.1% of the map area), Mixed Graminoid Freshwater Marsh (4.7% of the map area), and Black Mangrove Forest (4.5% of the map area). The NWCE vegetation map has a thematic class accuracy of 88.4% with a lower 90th Percentile Confidence Interval of 84.5%.
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