5,619 research outputs found

    Learning Multi-Tree Classification Models with Ant Colony Optimization

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    Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain

    ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

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    Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

    Ants constructing rule-based classifiers.

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    Classifiers; Data; Data mining; Studies;

    Ant colony optimization approach for stacking configurations

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    In data mining, classifiers are generated to predict the class labels of the instances. An ensemble is a decision making system which applies certain strategies to combine the predictions of different classifiers and generate a collective decision. Previous research has empirically and theoretically demonstrated that an ensemble classifier can be more accurate and stable than its component classifiers in most cases. Stacking is a well-known ensemble which adopts a two-level structure: the base-level classifiers to generate predictions and the meta-level classifier to make collective decisions. A consequential problem is: what learning algorithms should be used to generate the base-level and meta-level classifier in the Stacking configuration? It is not easy to find a suitable configuration for a specific dataset. In some early works, the selection of a meta classifier and its training data are the major concern. Recently, researchers have tried to apply metaheuristic methods to optimize the configuration of the base classifiers and the meta classifier. Ant Colony Optimization (ACO), which is inspired by the foraging behaviors of real ant colonies, is one of the most popular approaches among the metaheuristics. In this work, we propose a novel ACO-Stacking approach that uses ACO to tackle the Stacking configuration problem. This work is the first to apply ACO to the Stacking configuration problem. Different implementations of the ACO-Stacking approach are developed. The first version identifies the appropriate learning algorithms in generating the base-level classifiers while using a specific algorithm to create the meta-level classifier. The second version simultaneously finds the suitable learning algorithms to create the base-level classifiers and the meta-level classifier. Moreover, we study how different kinds on local information of classifiers will affect the classification results. Several pieces of local information collected from the initial phase of ACO-Stacking are considered, such as the precision, f-measure of each classifier and correlative differences of paired classifiers. A series of experiments are performed to compare the ACO-Stacking approach with other ensembles on a number of datasets of different domains and sizes. The experiments show that the new approach can achieve promising results and gain advantages over other ensembles. The correlative differences of the classifiers could be the best local information in this approach. Under the agile ACO-Stacking framework, an application to deal with a direct marketing problem is explored. A real world database from a US-based catalog company, containing more than 100,000 customer marketing records, is used in the experiments. The results indicate that our approach can gain more cumulative response lifts and cumulative profit lifts in the top deciles. In conclusion, it is competitive with some well-known conventional and ensemble data mining methods

    Intelligent Hypothermia Care System using Ant Colony Optimization for Rules Prediction

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    ان انخفاض درجة حرارة جسم المريض بعد اجراء العمليات الجراحية هو مصدر قلق كبير لكثير من الاطباء بعد الجراحة. يقدم هذا البحث نظاماً ذكياً (IHCS) لمراقبة انخفاض درجة الحرارة وبناء  على ذلك يتم تحديد اين سيرسل المرضى في جناح النقاهة. تتم عملية اتخاذ القرار بالاعتماد على مجموعة نتائج لفحوصات طبية حيث تمثل بيانات مدخلة للنظام (IHCS). يقوم هذا النظام بتصنيف البيانات المدخلة للتنبؤ بحالة المريض وتحديد وجهته القادمة كوحدة العناية المركزة او القسم العام داخل المستشفى او امكانية ارساله الى المنزل. ان الخوارزمية المستخدمة  في هذا البحث تم اختيارها بعد اختبار بيانات المرضى على مجموعة خوازميات وهي اولا:  خوارزمية شجرة القرارات, ثانيا خوارزمية النمل حيث تم استخدام قابليات النمل للبحث وايجاد اقصر مسار للطعام وتوظيفها لاستكشاف افضل القوانين للتنبؤ بحالة المريض.  واخيرا تم استخدام بعض تقنيات تهيئة وتجهيز البيانات قبل معالجتها ومن ثم معالجتها بواسطة نظرية المعلومات. وبعد تقييم النتائج اتضح ان خوارزمية مستعمرة النمل تعطي افضل النتائج فتمت برمجة القوانين المستخرجة والحصول على النظام الذكي (IHCS) لمساعدة الاطباء في عملية معالجة حالات انخفاض درجة حرارة الجسم بعد العمليات الجراحية وينفرد هذا البحث لاستخدامه هذه الخوارزمية لاول مرة في هذا المجال .Intelligent Hypothermia Care System (IHCS) is an intelligence system uses set of methodologies, algorithms, architectures, and processes to determine where patients in a postoperative recovery area must be sent. Hypothermia is a significant concern after surgery. This paper utilizes the classification task in data mining to propose an intelligent technique to predict where to send a patient after surgery: intensive care unit, general floor or home. To achieve this goal, this paper evaluates the performance of decision tree algorithm, exemplifying the deterministic approach, against the AntMiner algorithm, exemplifying the heuristic approach, to choose the best approach in detecting the patient’s status. Results show the outperformance of the heuristic approach. The implication of this proposal will be twofold: in hypothermia treatment and in the application of ant colony optimization

    Tour recommendation for groups

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    Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data

    Extensions to the ant-miner classification rule discovery algorithm

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    Ant-Miner is an application of ACO in data mining. It has been introduced by Parpinelli et al. in 2002 as an ant-based algorithm for the discovery of classification rules. Ant-Miner has proved to be a very promising technique for classification rules discovery. Ant-Miner generates a fewer number of rules, fewer terms per each rule and performs competitively in terms of efficiency compared to the C4.5 algorithm (see experimental results in [20]). Hence, it has been a focus area of research and a lot of modification has been done to it in order to increase its quality in terms of classification accuracy and output rules comprehensibility (reducing the size of the rule set). The thesis proposes five extensions to Ant-Miner. 1) The thesis proposes the use of a logical negation operator in the antecedents of constructed rules, so the terms in the rule antecedents could be in the form of . This tends to generate rules with higher coverage and reduce the size of the generated rule set. 2) The thesis proposes the use stubborn ants, an ACO-variation in which an ant is allowed to take into consideration its own personal past history. Stubborn ants tend to generate rules with higher classification accuracy in fewer trials per iteration. 3) The thesis proposes the use multiple types of pheromone; one for each permitted rule class, i.e. an ant would first select the rule class and then deposit the corresponding type of pheromone. The multi-pheromone system improves the quality of the output in terms of classification accuracy as well as it comprehensibility. 4) Along with the multi-pheromone system, the thesis proposes a new pheromone update strategy, called quality contrast intensifier. Such a strategy rewards rules with high confidence by depositing more pheromone and penalizes rules with low confidence by removing pheromone. 5) The thesis proposes that each ant to have its own value of α and β parameters, which in a sense means that each ant has its own individual personality. In order to verify the efficiency of these modifications, several cross-validation experiments have been applied on each of eight datasets used in the experiment. Average output results have been recorded, and a test of statistical significance has been applied to indicate improvement significance. Empirical results show improvements in the algorithm\u27s performance in terms of the simplicity of the generated rule set, the number of trials, and the predictive accuracy

    Optimization of Association Rule Using Heuristic Approach

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    Apriori algorithm is used to create all possible association rules among the items in the database, on the behalf of Association Rule Mining and Apriori Algorithm. Here proposed a new algorithm based on the Ant Colony Optimization algorithm to improve the result of association rule mining. Ant Colony Optimization (ACO) is a meta-heuristic approach that inspired by the real behaviour of ant colonies. The association rules create by Apriori algorithm after that find the rules from weakest set based on threshold value that will used the Ant Colony algorithm to reduce the association rules and discover the better quality of rules than apriori. In this research work proposed method focuses on reducing the scans of datasetss by optimization and improving the quality of rules generated for ACO

    An Order-based Algorithm for Minimum Dominating Set with Application in Graph Mining

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    Dominating set is a set of vertices of a graph such that all other vertices have a neighbour in the dominating set. We propose a new order-based randomised local search (RLSo_o) algorithm to solve minimum dominating set problem in large graphs. Experimental evaluation is presented for multiple types of problem instances. These instances include unit disk graphs, which represent a model of wireless networks, random scale-free networks, as well as samples from two social networks and real-world graphs studied in network science. Our experiments indicate that RLSo_o performs better than both a classical greedy approximation algorithm and two metaheuristic algorithms based on ant colony optimisation and local search. The order-based algorithm is able to find small dominating sets for graphs with tens of thousands of vertices. In addition, we propose a multi-start variant of RLSo_o that is suitable for solving the minimum weight dominating set problem. The application of RLSo_o in graph mining is also briefly demonstrated
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