The development of hashing indexing technique in case retrieval

Case-based reasoning (CBR) considers previous experience in form of cases to overcome new problems. It requires many solved cases in case base in order to produce a quality decision. Since today, database technology has allowed CBR to use a huge case storage therefore the case retrieval process also reflects the final decision in CBR. Traditionally, sequential indexing method has been applied to search for possible cases in case base. This technique is worked fast when the number of cases is small but it consumes more time to retrieve when the number of data grows in case base.To overcome the weakness, this study researches the non-sequential indexing called hashing as an alternative to mine large cases and faster the retrieval time in CBR. Hashing indexing searches a record by determines the index using only an entry's search key without traveling to all records. This paper presents the review of a literature and early stages of the integration hashing indexing method in CBR. The concept of hashing indexing in case retrieving process, the model development, and the preliminary algorithm testing result will be discussed in this paper

DERMA: A Melanoma Diagnosis Platform Based on Collaborative Multilabel Analog Reasoning

The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multilabel case-based reasoning subsystems called DERMA. The system has to face up several challenges that include data characterization, pattern matching, reliable diagnosis, and self-explanation capabilities. Experiments using subsystems specialized in confocal and dermoscopy images have provided promising results for helping experts to assess melanoma diagnosis

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains

. This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain. Keywords: Technical diagnosis, case retrieval, memory structures 1 Introduction In the area of Case-Based Reasoning (CBR), a major focus of research in recent years has been on the development of techniques allowing for an efficient retrieval of relevant cases in a given problem situation. This has led to a number of sophisticated techniques for this subtask, as for example indexing techniques ([11]); kd--trees ([14, 15]); the "Fish--and--Sink" approach ([12]); the Crash memory model ([3]); and Knowledge-directed Spreading Activation (KDSA, [16]). As an alternative memory model we have developed the model of Case Retrieval Nets (CRNs..

Ajuda al Diagnòstic de Càncer de Melanoma amb Raonament Analògic Multietiqueta

La mortalitat provocada pel càncer de melanoma ha augmentat en els últims anys a causa, principalment, dels nous hàbits d'exposició al sol. Atenent al criteri mèdic, el diagnòstic precoç s'ha convertit en el millor mètode de prevenció. No és però una tasca trivial ja que els experts del domini han de fer front a un problema caracteritzat per tenir un gran volum de dades, de format heterogeni i amb coneixement parcial. A partir d'aquestes necessitats es proposa la creació d'una eina de suport a la presa de decisions que sigui capaç d'ajudar els experts en melanoma en el seu diagnòstic. El sistema ha de fer front a diversos reptes plantejats, que inclouen la caracterització del domini, la identificació de patrons a les dades segons el criteri dels experts, la classificació de nous pacients i la capacitat d'explicar els pronòstics obtinguts. Aquestes fites s'han materialitzat en la plataforma DERMA, la qual està basada en la col•laboració de diversos subsistemes de raonament analògic multietiqueta. L'experimentació realitzada amb el sistema proposat utilitzant dades d'imatges confocals i dermatoscòpiques ha permès comprovar la fiabilitat del sistema. Els resultats obtinguts han estat validats pels experts en el diagnòstic del melanoma considerant-los positius.La mortalidad a causa del cáncer de melanoma ha aumentado en los últimos años debido, principalmente, a los nuevos hábitos de exposición al sol. Atendiendo al criterio médico, el diagnóstico precoz se ha convertido en el mejor método de prevención, pero no se trata de una tarea trivial puesto que los expertos del dominio deben hacer frente a un problema caracterizado por tener un gran volumen de datos, de formato heterogéneo y con conocimiento parcial. A partir de estas necesidades se propone la creación de una herramienta de ayuda a la toma de decisiones que sea capaz de ayudar a los expertos en melanoma en su diagnóstico. El sistema tiene que hacer frente a diversos retos planteados, que incluyen la caracterización del dominio, la identificación de patrones en los datos según el criterio médico, la clasificación de nuevos pacientes y la capacidad de explicar los pronósticos obtenidos. Estas metas se han materializado en la plataforma DERMA la cual está basada en la colaboración de varios subsistemas de razonamiento analógico multietiqueta. La experimentación realizada con el sistema propuesto utilizando datos de imágenes confocales y dermatoscópicas ha permitido verificar la fiabilidad del sistema. Los resultados obtenidos han sido validados por los expertos en el diagnóstico del melanoma considerándolos positivos.Mortality related to melanoma cancer has increased in recent years, mainly due to new habits of sun exposure. Considering the medical criteria, early diagnosis has become the best method of prevention but this is not trivial because experts are facing a problem characterized by a large volume of data, heterogeneous, and with partial knowledge. Based on these requirements we propose the creation of a decision support system that is able to assist experts in melanoma diagnosis. The system has to cope with various challenges, that include the characterization of the domain, the identification of data patterns attending to medical criteria, the classification of new patients, and the ability to explain predictions. These goals have been materialized in DERMA platform that is based on the collaboration of several analogical reasoning multi-label subsystems. The experiments conducted with the proposed system using confocal and dermoscopic images data have been allowed to ascertain the reliability of the system. The results have been validated by experts in diagnosis of melanoma considering it as positive

Modelo para incorporar conhecimento baseado em experiências à arquitetura TMN

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico.O ambiente de redes e serviços de telecomunicações é um sistema complexo. Como tal, o seu gerenciamento também é de alta complexidade. As experiências práticas, adquiridas ao longo dos anos e acumuladas individualmente por profissionais da área, são fundamentais para o sucesso e qualidade das atividades de gerenciamento de redes e serviços de telecomunicações. A Arquitetura de Informação TMN não faz referência, não orienta e não dispõe de elementos para representar conhecimento oriundo de experiências práticas em gerenciamento. Experiências estas que contém importantes informações em como gerenciar. Este trabalho propõe um modelo para incorporar conhecimento advindo de experiências em gerenciamento de redes e serviços à Arquitetura TMN, através da abordagem de Raciocínio Baseado em Casos. O modelo proposto foi concebido em acordo com a filosofia e recomendações do ITU-T, orientado a objetos, aberto e padronizado. Introduz à Arquitetura TMN diferentes construções e templates genéricos que permitem representar conhecimento advindo de diferentes experiências práticas de gerenciamento, criar bases de conhecimento formais e, em conseqüência, viabilizar o desenvolvimento de sistemas abertos de conhecimento

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets

Facing-up Challenges of Multiobjective Clustering Based on Evolutionary Algorithms: Representations, Scalability and Retrieval Solutions

Aquesta tesi es centra en algorismes de clustering multiobjectiu, que estan basats en optimitzar varis objectius simultàniament obtenint una col•lecció de solucions potencials amb diferents compromisos entre objectius. El propòsit d'aquesta tesi consisteix en dissenyar i implementar un nou algorisme de clustering multiobjectiu basat en algorismes evolutius per afrontar tres reptes actuals relacionats amb aquest tipus de tècniques. El primer repte es centra en definir adequadament l'àrea de possibles solucions que s'explora per obtenir la millor solució i que depèn de la representació del coneixement. El segon repte consisteix en escalar el sistema dividint el conjunt de dades original en varis subconjunts per treballar amb menys dades en el procés de clustering. El tercer repte es basa en recuperar la solució més adequada tenint en compte la qualitat i la forma dels clusters a partir de la regió més interessant de la col•lecció de solucions ofertes per l’algorisme.Esta tesis se centra en los algoritmos de clustering multiobjetivo, que están basados en optimizar varios objetivos simultáneamente obteniendo una colección de soluciones potenciales con diferentes compromisos entre objetivos. El propósito de esta tesis consiste en diseñar e implementar un nuevo algoritmo de clustering multiobjetivo basado en algoritmos evolutivos para afrontar tres retos actuales relacionados con este tipo de técnicas. El primer reto se centra en definir adecuadamente el área de posibles soluciones explorada para obtener la mejor solución y que depende de la representación del conocimiento. El segundo reto consiste en escalar el sistema dividiendo el conjunto de datos original en varios subconjuntos para trabajar con menos datos en el proceso de clustering El tercer reto se basa en recuperar la solución más adecuada según la calidad y la forma de los clusters a partir de la región más interesante de la colección de soluciones ofrecidas por el algoritmo.This thesis is focused on multiobjective clustering algorithms, which are based on optimizing several objectives simultaneously obtaining a collection of potential solutions with different trade¬offs among objectives. The goal of the thesis is to design and implement a new multiobjective clustering technique based on evolutionary algorithms for facing up three current challenges related to these techniques. The first challenge is focused on successfully defining the area of possible solutions that is explored in order to find the best solution, and this depends on the knowledge representation. The second challenge tries to scale-up the system splitting the original data set into several data subsets in order to work with less data in the clustering process. The third challenge is addressed to the retrieval of the most suitable solution according to the quality and shape of the clusters from the most interesting region of the collection of solutions returned by the algorithm