Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets

Modeling Internet as a User-Adapted Speech Service

Proceedings of: 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012.The web has become the largest repository of multimedia information and its convergence with telecommunications is now bringing the benefits of web technology and hybrid artificial intelligence systems to hand-held devices. However, maximizing accessibility is not always the main objective in the design of web applications, specially if it is concerned with facilitating access for disabled people. This way, natural spoken conversation and multimodal conversational agents have been proposed as a solution to facilitate a more natural interaction with these kind of devices. In this paper, we describe a proposal to provide spoken access to Internet information that is valid not only to generate basic applications (e.g., web search engines), but also to develop dialog-based speech interfaces that facilitate a user-adapted access that enhances web services. We describe our proposal and detail several applications developed to provide evidences about the benefits of introducing speech to make the enormous web content accessible to all mobile phone users.Research funded by projects CICYT TIN2011-28620- C02-01, CICYT TEC2011-28626-C02-02,CAM CONTEXTS (S2009/TIC-1485), and DPS2008-07029-C02-02.Publicad

Unsupervised Ensembles Techniques for Visualization

In this paper we introduce two unsupervised techniques for visualization purposes based on the use of ensemble methods. The unsupervised techniques which are often quite sensitive to the presence of outliers are combined with the ensemble approaches in order to overcome the influence of outliers. The first technique is based on the use of Principal Component Analysis and the second one is known for its topology preserving characteristics and is based on the combination of the Scale Invariant Map and Maximum Likelihood Hebbian learning. In order to show the advantage of these novel ensemble-based techniques the results of some experiments carried out on artificial and real data sets are included

Intelligent Systems in Context-Based Distributed Information Fusion

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IDS Based on Bio-inspired Models

Unsupervised projection approaches can support Intrusion Detection Systems for computer network security. The involved technologies assist a network manager in detecting anomalies and potential threats by an intuitive display of the progression of network traffic. Projection methods operate as smart compression tools and map raw, high-dimensional traffic data into 2-D or 3-D spaces for subsequent graphical display. The paper compares three projection methods, namely, Cooperative Maximum Likelihood Hebbian Learning, Auto-Associative Back-Propagation networks and Principal Component Analysis. Empirical tests on anomalous situations related to the Simple Network Management Protocol (SNMP) confirm the validity of the projection-based approach. One of these anomalous situations (the SNMP community search) is faced by these projection models for the first time. This work also highlights the importance of the time-information dependence in the identification of anomalous situations in the case of the applied methods

Improving the k-Nearest Neighbour Rule by an Evolutionary Voting Approach

This work presents an evolutionary approach to modify the voting system of the k-Nearest Neighbours (kNN). The main novelty of this article lies on the optimization process of voting regardless of the distance of every neighbour. The calculated real-valued vector through the evolutionary process can be seen as the relative contribution of every neighbour to select the label of an unclassified example. We have tested our approach on 30 datasets of the UCI repository and results have been compared with those obtained from other 6 variants of the kNN predictor, resulting in a realistic improvement statistically supported

A Case-based Reasoning System for Monitoring the Longevity of Dental Restorations.

Changes in dental restoration treatment patterns, combined with the introduction of new improved restorative materials and techniques, has greatly influenced the longevity of dental restorations. During the last decades there have been a great advantage in the restoration field. Since mid -1980’s adhesive resin systems have been advocated for use in bonding amalgam to tooth structure and the advantages of bonded amalgams have been proved. The aim of this paper is to show how is evolving the field of the dental restoration and how composite restoration is gaining the battle to amalgam restoration with the help of a case-based reasoning system. This problem solving technique has been used to monitor the evolution of the dental restoration over the last decade using historical data and expert knowledg

CBR model for the intelligent management of customer support centers

[EN] In this paper, a new CBR system for Technology Management Centers is presented. The system helps the staff of the centers to solve customer problems by finding solutions successfully applied to similar problems experienced in the past. This improves the satisfaction of customers and ensures a good reputation for the company who manages the center and thus, it may increase its profits. The CBR system is portable, flexible and multi-domain. It is implemented as a module of a help-desk application to make the CBR system as independent as possible of any change in the help-desk. Each phase of the reasoning cycle is implemented as a series of configurable plugins, making the CBR module easy to update and maintain. This system has been introduced and tested in a real Technology Management center ran by the Spanish company TISSAT S.A.Financial support from Spanish government under grant PROFIT FIT-340001-2004-11 is gratefully acknowledgeHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Julian Inglada, VJ.; Rebollo Pedruelo, M.; Botti, V. (2006). CBR model for the intelligent management of customer support centers. En Lecture Notes in Computer Science. Springer Verlag (Germany). 663-670. https://doi.org/10.1007/11875581_80S663670Acorn, T., Walden, S.: SMART: SupportManagement Automated Reasoning Technology for Compaq Customer Service. In: Scott, A., Klahr, P. (eds.) Proceedings of the 2 International Conference on Intelligent Tutoring Systems, ITS-92 Berlin, vol. 4, pp. 3–18. AAAI Press, Menlo Park (1992)Simoudis, E.: Using Case-Based Retrieval for Customer Technical Support. IEEE Intelligent Systems 7, 10–12 (1992)Kriegsman, M., Barletta, R.: Building a Case-Based Help Desk Application. IEEE Expert: Intelligent Systems and Their Applications 8, 18–26 (1993)Shimazu, H., Shibata, A., Nihei, K.: Case-Based Retrieval Interface Adapted to Customer-Initiated Dialogues in Help Desk Operations. In: Mylopoulos, J., Reiter, R. (eds.) Proceedings of the 12th National Conference on Artificial Intelligence, vol. 1, pp. 513–518. AAAI Press, Menlo Park (1994)Raman, R., Chang, K.H., Carlisle, W.H., Cross, J.H.: A self-improving helpdesk service system using case-based reasoning techniques. Computers in Industry 2, 113–125 (1996)Kang, B.H., Yoshida, K., Motoda, H., Compton, P.: Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11, 611–631 (1997)Roth-Berghofer, T., Iglezakis, I.: Developing an Integrated Multilevel Help-Desk Support System. In: Proceedings of the 8th German Workshop on Case-Based Reasoning, pp. 145–155 (2000)Goker, M., Roth-Berghofer, T.: The development and utilization of the case-based help-desk support system HOMER. Engineering Applications of Artificial Intelligence 12, 665–680 (1999)Roth-Berghofer, T.R.: Learning from HOMER, a case-based help-desk support system. In: Melnik, G., Holz, H. (eds.) Advances in Learning Software Organizations, pp. 88–97. Springer, Heidelberg (2004)Bergmann, R., Althoff, K.D., Breen, S., Göker, M., Manago, M., Traphöner, R., Wess, S.: Developing Industrial Case-Based Reasoning Applications. In: The INRECA Methodology, 2nd edn. LNCS (LNAI), vol. 1612. Springer, Heidelberg (2003)eGain (2006), http://www.egain.comKaidara Software Corporation (2006), http://www.kaidara.com/Empolis Knowledge Management GmbH - Arvato AG (2006), http://www.empolis.com/Althoff, K.D., Auriol, E., Barletta, R., Manago, M.: A Review of Industrial Case-Based Reasoning Tools. AI Perspectives Report. Goodall, A., Oxford (1995)Watson, I.: Applying Case-Based Reasoning. Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc. California (1997)empolis: empolis Orenge Technology Whitepaper. Technical report, empolis GmbH (2002)Tissat, S.A. (2006), http://www.tissat.esGiraud-Carrier, C., Martinez, T.R.: An integrated framework for learning and reasoning. Journal of Artificial Intelligence Research 3, 147–185 (1995)Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yanez, J.C.: Neuro-symbolic system for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 1–10. Springer, Heidelberg (2004)Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1997

SpamHunting: An instance-based reasoning system for spam labelling and filtering

n this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable en-hanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the tar-get case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules