Real-world problems usually present a huge volume of imprecise data. These types of problems may challenge case-based reasoning systems because the knowledge extracted from data is used to identify ...analogies and solve new problems. Many authors have focused on organizing case memory in patterns to minimize the computational burden and deal with uncertainty. The organization is usually determined by a single criterion, but in some problems, a single criterion can be insufficient to find accurate clusters. This work describes an approach to organize the case memory in patterns based on multiple criteria. This new approach uses the searching capabilities of multiobjective evolutionary algorithms to build a Pareto set of solutions, where each one is a possible organization based on the relevance of objectives. The system shows promising capabilities when it is compared with a successful system based on self-organizing maps. Due to the data set geometry influences, the clustering building process results are analyzed taking into account it. For this reason, some complexity measures are used to categorize data sets according to their topology.
DESMAI is a framework for helping experts in breast cancer diagnosis. It allows experts to explore digital mammographic image databases according to a certain topology criteria when they need to ...decide whether a sample is benign or malignant. In this way, they are provided with complementary information to enhance their interpretations and predictions. The core of the application is a SOMCBR system, which is variant of a Case-Based Reasoning system featured by organizing the case memory using a Self-Organizing Map. The article presents a strategy for improving the SOMCBR reliability thanks to the relations between cases and clusters. The approach is successfully applied in DESMAI for estimating, if it is possible, the class of the recovered mammographies.
Some of the real-world problems are represented with just one label but many of today’s issues are currently being defined with multiple labels. This second group is important because multi-label ...classes provide a more global picture of the problem. From the study of the characteristics of the most influential systems in this area, MlKnn and RAkEL, we can observe that the main drawback of these specific systems is the time required. Therefore, the aim of the current paper is to develop a more efficient system in terms of computation without incurring accuracy loss. To meet this objective we propose MlCBR, a system for multi-label classification based on Case-Based Reasoning. The results obtained highlight the strong performance of our algorithm in comparison with previous benchmark methods in terms of accuracy rates and computational time reduction.
Network security tests should be periodically conducted to detect vulnerabilities before they are exploited. However, analysis of testing results is resource intensive with many data and requires ...expertise because it is an unsupervised domain. This paper presents how to automate and improve this analysis through the identification and explanation of device groups with similar vulnerabilities. Clustering is used for discovering hidden patterns and abnormal behaviors. Self-organizing maps are preferred due to their soft computing capabilities. Explanations based on anti-unification give comprehensive descriptions of clustering results to analysts. This approach is integrated in
Consensus, a computer-aided system to detect network vulnerabilities.
Information system security must battle regularly with new threats that jeopardize the protection of those systems. Security tests have to be run periodically not only to identify vulnerabilities but ...also to control information systems, network devices, services and communications. Vulnerability assessments gather large amounts of data to be further analyzed by security experts, who recently have started using data analysis techniques to extract useful knowledge from these data. With the aim of assisting this process, this work presents CAOS, an evolutionary multiobjective approach to be used to cluster information of security tests. The process enables the clustering of the tested devices with similar vulnerabilities to detect hidden patterns, rogue or risky devices. Two different types of metrics have been selected to guide the discovery process in order to get the best clustering solution: general-purpose and specific-domain objectives. The results of both approaches are compared with the state-of-the-art single-objective clustering techniques to corroborate the benefits of the clustering results to security analysts.
This article addresses breast cancer diagnosis using mammographic images. Throughout, the diagnosis is done using the mammographic microcalcifications. The aim of the work presented here is twofold. ...First, we introduce a back-end phase, based on machine learning techniques, in a previous computer aided diagnosis system. The two machine learning techniques incorporated are case-based reasoning and genetic algorithms. These algorithms look for improving the results obtained by human experts and the previous statistical model. On the other hand, we analyse the obtained results comparing them with the ones provided by other well-known machine learning techniques. The breast cancer dataset used in the experiments come from Girona Health Area. This database contains 216 images previously diagnosed by surgical biopsy.
Nowadays the educational methodologies are being reconsidered to allow the successful achievement of the skills of the future computer engineer. The issue is based on adapting these methodologies ...from the point of view of the competences provided by the subjects. This work in progress focuses on identifying typologies subjects through data mining techniques. This categorization allows experts to study the best way of adapting the educational models in order to guarantee the acquisition of the expected competences.
Even though there are many tutoring systems to help students achieve their goals in terms of theoretical knowledge, as yet there is no system to foment the acquisition of the competences which form ...an integral part of university degree programs. This issue is crucial because the Higher Education System is changing in Europe. New educational models are being created to introduce competences which correspond specifically to degree programs. In this work-in-progress the general framework to develop an Intelligent Tutoring System (ITS) based on competences is presented. The system monitorizes the acquisition of theoretical knowledge and also the assessment of the competences related to the subject and it takes corrective actions when needed to fix a negative evolution of the student. The framework is divided into four main phases based on artificial intelligence techniques: construction, location, prediction and reinforcement. The main feature of the proposed framework is the fact that it promotes the academic development of the student in the future educational context by providing guidance and supervision to ensure the successful acquisition of both theoretical knowledge and the corresponding subject-related competences.
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