Journal articles on the topic 'Classification rule'
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1
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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Shadman Roodposhti, Majid, Arko Lucieer, Asim Anees, and Brett Bryan. "A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification." Remote Sensing 11, no. 17 (September 1, 2019): 2057. http://dx.doi.org/10.3390/rs11172057.
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This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.
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Pal, Parashu Ram, Pankaj Pathak, and Shkurte Luma-Osmani. "IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification." Journal of Information & Knowledge Management 20, no. 01 (March 2021): 2150010. http://dx.doi.org/10.1142/s0219649221500106.
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Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.
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Inuiguchi, Masahiro, and Keisuke Washimi. "Improving Rough Set Rule-Based Classification by Supplementary Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 747–58. http://dx.doi.org/10.20965/jaciii.2015.p0747.
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In rough set approaches, decision rules are induced from a given data set consisting of attribute values and a decision value. Induced rules are used to classify new objects, but this classification is not perfect, perhaps because the given data set does not include all possible patterns. No induced decision rules are matched totally for objects having missing patterns, and partially matched decision rules are used to estimate their classes. The classification accuracy of such an object is usually lower than that of an object totally matching decision rules. To improve the classification accuracy, we propose adding supplementary rules to the induced rules, defining the supplementary rules to improve the classification accuracy of objects only partially matching decision rules. We propose an algorithm for inducing supplementary rules, considering four classifiers consisting of supplementary rules together with originally induced rules.We investigate their performance. We also compare their classification accuracies to that of conventional classifier with originally induced rules.
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Thomas, Binu, and G. Raju. "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules." ISRN Artificial Intelligence 2013 (December 19, 2013): 1–10. http://dx.doi.org/10.1155/2013/316913.
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In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.
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Li, Liang Jun, Bin Zhang, Yuan Yuan Che, Ming Yang, and Tie Nan Li. "Self-Adaptive Weighting Text Association Categorization Algorithm Research." Advanced Materials Research 171-172 (December 2010): 246–51. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.246.
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In text association classification research, feature distribution of the training sample collection impacts greatly on the classification results, even with a same classification algorithm classification results will have obvious differences using different sample collections. In order to solve the problem, the stability of association classification is improved by the weighing method in the paper, the design realizes the association classification algorithms (WARC) based on rule weight. In the WARC algorithm, this paper proposes the concept of classification rule intensity and gives the concrete formula. Using rule intensity defines the rule adjustment factors that adjust uneven classification rules. Experimental results show the accuracy of text classification can be improved obviously by self-adaptive weighting.
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Ghosh, Bishwamittra, Dmitry Malioutov, and Kuldeep S. Meel. "Efficient Learning of Interpretable Classification Rules." Journal of Artificial Intelligence Research 74 (August 30, 2022): 1823–63. http://dx.doi.org/10.1613/jair.1.13482.
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Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpretable models, where the prediction is understandable to a human. In interpretable machine learning, rule-based classifiers are particularly effective in representing the decision boundary through a set of rules comprising input features. Examples of such classifiers include decision trees, decision lists, and decision sets. The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable. To learn such a classifier, the brute-force direct approach is to consider an optimization problem that tries to learn the smallest classification rule that has close to maximum accuracy. This optimization problem is computationally intractable due to its combinatorial nature and thus, the problem is not scalable in large datasets. To this end, in this paper we study the triangular relationship among the accuracy, interpretability, and scalability of learning rule-based classifiers. The contribution of this paper is an interpretable learning framework IMLI, that is based on maximum satisfiability (MaxSAT) for synthesizing classification rules expressible in proposition logic. IMLI considers a joint objective function to optimize the accuracy and the interpretability of classification rules and learns an optimal rule by solving an appropriately designed MaxSAT query. Despite the progress of MaxSAT solving in the last decade, the straightforward MaxSAT-based solution cannot scale to practical classification datasets containing thousands to millions of samples. Therefore, we incorporate an efficient incremental learning technique inside the MaxSAT formulation by integrating mini-batch learning and iterative rule-learning. The resulting framework learns a classifier by iteratively covering the training data, wherein in each iteration, it solves a sequence of smaller MaxSAT queries corresponding to each mini-batch. In our experiments, IMLI achieves the best balance among prediction accuracy, interpretability, and scalability. For instance, IMLI attains a competitive prediction accuracy and interpretability w.r.t. existing interpretable classifiers and demonstrates impressive scalability on large datasets where both interpretable and non-interpretable classifiers fail. As an application, we deploy IMLI in learning popular interpretable classifiers such as decision lists and decision sets. The source code is available at https://github.com/meelgroup/mlic.
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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 (October 20, 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. Our approach consists of two phases: candidate rule generation and rule selection. We first propose a pre-screening method for generating a tractable number of promising candidate rules for high-dimensional classification problems where it is impossible to examine all combinations of antecedent linguistic values. Next we show how genetic algorithms can be applied to the rule selection. Then we combine a heuristic rule elimination procedure with genetic algorithms for improving their search ability. Finally, the performance of our approach is examined by computer simulations on commonly used data sets.
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Refai. "PARTIAL RULE MATCH FOR FILTERING RULES IN ASSOCIATIVE CLASSIFICATION." Journal of Computer Science 10, no. 4 (April 1, 2014): 570–77. http://dx.doi.org/10.3844/jcssp.2014.570.577.
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Afify, Ashraf A. "A fuzzy rule induction algorithm for discovering classification rules." Journal of Intelligent & Fuzzy Systems 30, no. 6 (April 30, 2016): 3067–85. http://dx.doi.org/10.3233/ifs-152034.
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Yousefi, Mohammadmahdi R., Jianping Hua, and Edward R. Dougherty. "Multiple-rule bias in the comparison of classification rules." Bioinformatics 27, no. 12 (May 5, 2011): 1675–83. http://dx.doi.org/10.1093/bioinformatics/btr262.
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Solihin, W., and C. Eastman. "Classification of rules for automated BIM rule checking development." Automation in Construction 53 (May 2015): 69–82. http://dx.doi.org/10.1016/j.autcon.2015.03.003.
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Li, Shasha, Zhongmei Zhou, and Weiping Wang. "Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/984375.
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The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule setsAandB. Every instance in training set can be covered by at least one rule not only in rule setA, but also in rule setB. In order to improve the quality of rule setB, we take measure to prune the length of rules in rule setB. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.
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Hichem, Haouassi, MEHDAOUI Rafik, and Chouhal Ouahiba. "New Discrete Crow Search Algorithm for Class Association Rule Mining." International Journal of Swarm Intelligence Research 13, no. 1 (January 2022): 1–21. http://dx.doi.org/10.4018/ijsir.2022010109.
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Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.
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Thabtah, Fadi, Suhel Hammoud, and Hussein Abdel-Jaber. "Parallel Associative Classification Data Mining Frameworks Based MapReduce." Parallel Processing Letters 25, no. 02 (June 2015): 1550002. http://dx.doi.org/10.1142/s0129626415500024.
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Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy.
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Wattenmaker, William D., Heather L. Mcquaid, and Stephanie J. Schwertz. "Analogical versus rule-based classification." Memory & Cognition 23, no. 4 (July 1995): 495–509. http://dx.doi.org/10.3758/bf03197250.
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Mojirsheibani, M. "A consistent combined classification rule." Statistics & Probability Letters 36, no. 1 (November 1997): 43–47. http://dx.doi.org/10.1016/s0167-7152(97)00047-3.
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Riid, Andri, and Jürgo-Sören Preden. "Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation." Journal of Artificial Intelligence and Soft Computing Research 7, no. 2 (April 1, 2017): 137–47. http://dx.doi.org/10.1515/jaiscr-2017-0010.
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AbstractThis paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.
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Ishibuchi, H., and T. Yamamoto. "Rule weight specification in fuzzy rule-based classification systems." IEEE Transactions on Fuzzy Systems 13, no. 4 (August 2005): 428–35. http://dx.doi.org/10.1109/tfuzz.2004.841738.
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Zhang, Shou Juan, and Quan Zhou. "A Novel Efficient Classification Algorithm Based on Class Association Rules." Applied Mechanics and Materials 135-136 (October 2011): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.106.
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A novel classification algorithm based on class association rules is proposed in this paper. Firstly, the algorithm mines frequent items and rules only in one phase. Then, the algorithm ranks rules that pass the support and confidence thresholds using a global sorting method according to a series of parameters, including confidence, support, antecedent cardinality, class distribution frequency, item row order and rule antecedent length. Classifier building is based on rule items that do not overlap in the training phase and rule items that each training instance is covered by only a single rule. Experimental results on the 8 datasets from UCI ML Repository show that the proposed algorithm is highly competitive when compared with the C4.5,CBA,CMAR and CPAR algorithms in terms of classification accuracy and efficiency. This algorithm can offer an available associative classification technique for data mining.
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Mattiev, Jamolbek, and Branko Kavsek. "Coverage-Based Classification Using Association Rule Mining." Applied Sciences 10, no. 20 (October 9, 2020): 7013. http://dx.doi.org/10.3390/app10207013.
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Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.
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Furuhashi, Takeshi. "Rule Extraction from Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (October 20, 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 special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.
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.S, Padmavathi, and M. Chidambaram. "A Brief Survey on Text Classification Using Various Machine Learning Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 1 (February 3, 2018): 14. http://dx.doi.org/10.23956/ijarcsse.v8i1.521.
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Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.
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KHOSHGOFTAAR, TAGHI M., LOFTON A. BULLARD, and KEHAN GAO. "A RULE-BASED SOFTWARE QUALITY CLASSIFICATION MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 03 (June 2008): 247–59. http://dx.doi.org/10.1142/s0218539308003064.
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A rule-based classification model is presented to identify high-risk software modules. It utilizes the power of rough set theory to reduce the number of attributes, and the equal frequency binning algorithm to partition the values of the attributes. As a result, a set of conjuncted Boolean predicates are formed. The model is inherently influenced by the practical needs of the system being modeled, thus allowing the analyst to determine which rules are to be used for classifying the fault-prone and not fault-prone modules. The proposed model also enables the analyst to control the number of rules that constitute the model. Empirical validation of the model is accomplished through a case study of a large legacy telecommunications system. The ease of rule interpretation and the transparency of the functional aspects of the model are clearly demonstrated. It is concluded that the new model is effective in achieving the software quality classification.
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Kang, Wan Li, Jing Jing Wu, Du Wu Cui, and Li Zhao. "An Oriented Clonal Selection Algorithm for Associative Classification." Applied Mechanics and Materials 170-173 (May 2012): 3320–23. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3320.
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In this paper, we present an oriented clonal selection algorithm (O-CLONALG) for mining association rules effectively for classification. Different with the traditional evolutionary algorithms, O-CLONALG firstly scans dataset one time to find the frequent rules with one item. The items are used to generate new rules and the mutation operation is limited in it. When mutation operation takes place, each rule in the same generation was added a new item. The results have shown that it is efficient in dealing with the problem on the complexity of the rule search space. At the same time, good classification accuracy has been achieved
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Celik, Mete, Fehim Koylu, and Dervis Karaboga. "CoABCMiner: An Algorithm for Cooperative Rule Classification System Based on Artificial Bee Colony." International Journal on Artificial Intelligence Tools 25, no. 01 (February 2016): 1550028. http://dx.doi.org/10.1142/s0218213015500281.
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In data mining, classification rule learning extracts the knowledge in the representation of IF_THEN rule which is comprehensive and readable. It is a challenging problem due to the complexity of data sets. Various meta-heuristic machine learning algorithms are proposed for rule learning. Cooperative rule learning is the discovery process of all classification rules with a single run concurrently. In this paper, a novel cooperative rule learning algorithm, called CoABCMiner, based on Artificial Bee Colony is introduced. The proposed algorithm handles the training data set and discovers the classification model containing the rule list. Token competition, new updating strategy used in onlooker and employed phases, and new scout bee mechanism are proposed in CoABCMiner to achieve cooperative learning of different rules belonging to different classes. We compared the results of CoABCMiner with several state-of-the-art algorithms using 14 benchmark data sets. Non parametric statistical tests, such as Friedman test, post hoc test, and contrast estimation based on medians are performed. Nonparametric tests determine the similarity of control algorithm among other algorithms on multiple problems. Sensitivity analysis of CoABCMiner is conducted. It is concluded that CoABCMiner can be used to discover classification rules for the data sets used in experiments, efficiently.
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Bologna, Guido. "A Simple Convolutional Neural Network with Rule Extraction." Applied Sciences 9, no. 12 (June 13, 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, followed by a fully connected layer. Rule extraction is performed on the fully connected layer, with the help of the Discretized Interpretable Multi Layer Perceptron (DIMLP). This transparent MLP architecture allows us to generate symbolic rules, by precisely locating axis-parallel hyperplanes. Experiments based on cross-validation emphasize that our approach is more accurate than that based on SVMs and decision trees that substitute DIMLPs. Overall, rules reach high fidelity and the discriminative n-grams represented in the antecedents explain the classifications adequately. With several test examples we illustrate the n-grams represented in the activated rules. They present the particularity to contribute to the final classification with a certain intensity.
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Almutairi, Manal, Frederic Stahl, and Max Bramer. "ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification." IEEE Access 9 (2021): 52015–35. http://dx.doi.org/10.1109/access.2021.3062763.
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Liu, Han, and Mihaela Cocea. "Induction of classification rules by Gini-index based rule generation." Information Sciences 436-437 (April 2018): 227–46. http://dx.doi.org/10.1016/j.ins.2018.01.025.
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Abdelhamid, Neda, Aladdin Ayesh, Fadi Thabtah, Samad Ahmadi, and Wael Hadi. "MAC: A Multiclass Associative Classification Algorithm." Journal of Information & Knowledge Management 11, no. 02 (June 2012): 1250011. http://dx.doi.org/10.1142/s0219649212500116.
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Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.
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Qiao, Litao, Weijia Wang, and Bill Lin. "Learning Accurate and Interpretable Decision Rule Sets from Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4303–11. http://dx.doi.org/10.1609/aaai.v35i5.16555.
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This paper proposes a new paradigm for learning a set of independent logical rules in disjunctive normal form as an interpretable model for classification. We consider the problem of learning an interpretable decision rule set as training a neural network in a specific, yet very simple two-layer architecture. Each neuron in the first layer directly maps to an interpretable if-then rule after training, and the output neuron in the second layer directly maps to a disjunction of the first layer rules to form the decision rule set. Our representation of neurons in this first rules layer enables us to encode both the positive and the negative association of features in a decision rule. State-of-the-art neural net training approaches can be leveraged for learning highly accurate classification models. Moreover, we propose a sparsity-based regularization approach to balance between classification accuracy and the simplicity of the derived rules. Our experimental results show that our method can generate more accurate decision rule sets than other state-of-the-art rule-learning algorithms with better accuracy-simplicity trade-offs. Further, when compared with uninterpretable black-box machine learning approaches such as random forests and full-precision deep neural networks, our approach can easily find interpretable decision rule sets that have comparable predictive performance.
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Jain, Deepti, and Divakar Singh. "A Review on associative classification for Diabetic Datasets A Simulation Approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 21, 2013): 533–38. http://dx.doi.org/10.24297/ijct.v7i1.3483.
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Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.
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Mohammadpour, Reza Ali, Seyed Mohammad Abedi, Somayeh Bagheri, and Ali Ghaemian. "Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/564867.
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The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the characteristics, the results of myocardial perfusion scan (MPS), and coronary artery angiography of 115 patients, 62 (53.9%) males, in Mazandaran Heart Center in the north of Iran have been collected. We used membership functions for medical variables by reviewing the related literature. To improve the classification performance, we used Ishibuchi et al. and Nozaki et al. methods by adjusting the grade of certaintyCFjof each rule. This system includes 144 rules and the antecedent part of all rules has more than one part. The coronary artery disease data used in this paper contained 115 samples. The data was classified into four classes, namely, classes 1 (normal), 2 (stenosis in one single vessel), 3 (stenosis in two vessels), and 4 (stenosis in three vessels) which had 39, 35, 17, and 24 subjects, respectively. The accuracy in the fuzzy classification based on if-then rule was 92.8 percent if classification result was considered based on rule selection by expert, while it was 91.9 when classification result was obtained according to the equation. To increase the classification rate, we deleted the extra rules to reduce the fuzzy rules after introducing the membership functions.
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Khraibet AL-Behadili, Hayder Naser, Ku Ruhana Ku-Mahamud, and Rafid Sagban. "Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1499. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1499-1507.
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<span>Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, </span><span>whereas exploration aims towards new promising regions during a search process. </span><span>The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. </span><span>Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and </span><span>computational time. </span><span>The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.</span>
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Alpert, Sofiia. "Analysis of “mixing” combination rules and Smet’s combination rule." Ukrainian journal of remote sensing, no. 23 (December 28, 2019): 4–8. http://dx.doi.org/10.36023/ujrs.2019.23.158.
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The process of solution of different practical and ecological problems, using hyperspectral satellite images usually includes a procedure of classification. Classification is one of the most difficult and important procedures. Some image classification methods were considered and analyzed in this work. These methods are based on the theory of evidence. Evidence theory can simulate uncertainty and process imprecise and incomplete information. It were considered such combination rules in this paper: “mixing” combination rule (or averaging), convolutive x-averaging (or c-averaging) and Smet’s combination rule. It was shown, that these methods can process the data from multiple sources or spectral bands, that provide different assessments for the same hypotheses. It was noted, that the purpose of aggregation of information is to simplify data, whether the data is coming from multiple sources or different spectral bands. It was shown, that Smet’s rule is unnormalized version of Dempster rule, that applied in Smet’s Transferable Belief Model. It also processes imprecise and incomplete data. Smet’s combination rule entails a slightly different formulation of Dempster-Shafer theory. Mixing (or averaging) rule was considered in this paper too. It is the averaging operation that is used for probability distributions. This rule uses basic probability assignments from different sources (spectral bands) and weighs assigned according to the reliability of the sources. Convolutive x-averaging (or c-averaging) rule was considered in this paper too. This combination rule is a generalization of the average for scalar numbers. This rule is commutative and not associative. It also was noted, that convolutive x-averaging (c-averaging) rule can include any number of basic probability assignments. It were also considered examples, where these proposed combination rules were used. Mixing, convolutive x-averaging (c-averaging) rule and Smet’s combination rule can be applied for analysis of hyperspectral satellite images, in remote searching for minerals and oil, solving different environmental and thematic problems.
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Loai Ali, A., F. Schmid, Z. Falomir, and C. Freksa. "TOWARDS RULE-GUIDED CLASSIFICATION FOR VOLUNTEERED GEOGRAPHIC INFORMATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 211–17. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-211-2015.
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Crowd-sourcing, especially in form of Volunteered Geographic Information (VGI) significantly changed the way geographic data is collected and the products that are generated from them. In VGI projects, contributors’ heterogeneity fosters rich data sources, however with problematic quality. In this paper, we tackle data quality from a <i>classification</i> perspective. Particularly in VGI, data classification presents some challenges: In some cases, the classification of entities depends on individual conceptualization about the environment. Whereas in other cases, a geographic feature itself might have ambiguous characteristics. These problems lead to inconsistent and inappropriate classifications. To face these challenges, we propose a guided classification approach. The approach employs data mining algorithms to develop a classifier, through investigating the geographic characteristics of target feature classes. The developed classifier acts to distinguish between related classes like <i>forest</i>, <i>meadow</i> and <i>park</i>. Then, the classifier could be used to guide the contributors during the classification process. The findings of an empirical study illustrate that the developed classifier correctly predict some classes. However, it still has a limited accuracy with other related classes.
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Feng, Xinghua, Kunpeng Wang, Jiangmei Zhang, and Jiayue Guan. "A New Measure for Determining the Equivalent Symmetry of Decomposed Subsystems from Large Complex Cyber–Physical Systems." Symmetry 15, no. 1 (December 23, 2022): 37. http://dx.doi.org/10.3390/sym15010037.
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In this paper, we propose a new consistency measurement for classification rule sets that is based on the similarity of their classification abilities. The similarity of the classification abilities of the two rule sets is evaluated though the similarity of the corresponding partitions of the feature space using the different rule sets. The proposed consistency measure can be used to measure the equivalent symmetry of subsystems decomposed from a large, complex cyber–physical system (CPS). It can be used to verify whether the same knowledge is obtained by the sensing data in the different subsystems. In the experiments, five decision tree algorithms and eighteen datasets from the UCI machine learning repository are employed to extract the classification rules, and the consistency between the corresponding rule sets is investigated. The classification rule sets extracted from the use of the C4.5 algorithm on the electrical grid stability dataset have a consistency of 0.88, which implies that the different subsystems contain almost equivalent knowledge about the network stability.
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Takagi, Noboru. "An Application of Binary Decision Trees to Pattern Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (September 20, 2006): 682–87. http://dx.doi.org/10.20965/jaciii.2006.p0682.
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Decision rules are a key technique in decision making, data mining and knowledge discovery in databases. We introduce an application of decision rules, handwriting pattern classification. When decision rules are applied to pattern recognition, one rule forms a hyperrectangle in feature space, i.e., each decision rule corresponds to one hyperrectangle. This means that a set of decision rules is considered a classification system, called the subclass method. We apply decision rules to handwritten Japanese character recognition, showing experimental results.
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Kovács, László. "Concept Lattice-Based Classification in NLP." Proceedings 63, no. 1 (December 24, 2020): 48. http://dx.doi.org/10.3390/proceedings2020063048.
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Classification in discrete object space is a widely used machine learning technique. In this case, we can construct a rule set using attribute level implication rules. In this paper, we apply the technique of formal concept analysis to generate the rule base of the classification. This approach is suitable for cases where the number of possible attribute subsets is limited. For testing of this approach, we investigated the problem of the part of speech prediction in natural language texts. The proposed model provides a better accuracy and execution cost than the baseline back-propagation neural network method.
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Dun, Yi Jie, Ya Bin Shao, and Shuang Liang Tian. "A New Method to Mine Classification Rules." Applied Mechanics and Materials 204-208 (October 2012): 4904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4904.
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This paper makes use of knowledge granular to present a new method to mine rules based on granule. First, use the measure to measure the importance of attribute, and get the granularity of the universe, and then repeat this procedure to every granule of the granularity, until the decision attribute has only one value for all granules, then we will describe every granule to get the rule. The analysis of the algorithm and the experiment show that the method presented is effective and reliable.Classification rules is the main target of association rule,decision tree and rough sets.a new algorithm to mine classification rules based on the importance of attribute value supported.this algorithm views the importance as the number of tuple pair that can be discernible by it,and the rules obtained from the constructed decision tree is equivalent to those obtained from ID3,which can be proved by the idea of rule fusion.however, this method is of low computation,and is more suitable to large database . rough sets is a techniques applied to data mining problems. This paper presents a new method to extract efficiently classification rules from decision table. The new model uses rough set theory to help in decreasing the computational effort needed for building decision tree by using what is called reduct algorithm and a rules set (knowledge) is generated from the decision table. reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing with traditional rough set approaches. Data mining research has made much effort to apply various mining algorithms efficiently on large databases.
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Lupi, Marino, Alessandro Farina, Antonio Pratelli, and Alessandra Gazzarri. "Application of classification rules to Italian ports." PROMET - Traffic&Transportation 26, no. 4 (August 20, 2014): 345–54. http://dx.doi.org/10.7307/ptt.v26i4.1434.
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In this paper, the existing rules commonly used for port traffic comparison are described. These rules provide weighting factors for each freight category in order to make them comparable and exploitable for port ranking. These rules are based on the value added concept related to port activities. Two new rules are proposed. The first is again based on the value added concept. The second rule is based on the assumption that ports not only create labour directly, through activities related to port operations, but they also play the role of “gates” for the existing economic activities of a region or a country, as a consistent quota of the overall international trade takes place by sea. This rule is based on the relationship among the trend of traffic volume of each freight category and the trend of the national GDP. The rules existing in the literature and the proposed new rules have been applied in ranking Italian ports; the results are discussed. The sensitivity of the ranking of Italian ports, to the different weighting rules, has been analysed.