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Dive into the research topics where Jose Manuel Zurita is active.

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Featured researches published by Jose Manuel Zurita.


Fuzzy Sets and Systems | 1999

Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems

Juan Luis Castro; Jose Jesus Castro-Schez; Jose Manuel Zurita

The aim of this article is to present a new approach to machine learning (precisely in classification problems) in which the use of fuzzy logic has been taken into account. We intend to show that fuzzy logic introduces new elements in the identification process, mainly due to the facility to manage imprecise information. An inductive algorithm generating a set of fuzzy rules identifying the system will be achieved. The maximal structure of a fuzzy rule will be found using this algorithm.


Information Sciences | 2009

Loss and gain functions for CBR retrieval

Juan Luis Castro; María Navarro; José Sánchez; Jose Manuel Zurita

The method described in this article evaluates case similarity in the retrieval stage of case-based reasoning (CBR). It thus plays a key role in deciding which case to select, and therefore, in deciding which solution will be eventually applied. In CBR, there are many retrieval techniques. One feature shared by most is that case retrieval is based on attribute similarity and importance. However, there are other crucial factors that should be considered, such as the possible consequences of a given solution, in other words its potential loss and gain. As their name clearly implies, these concepts are defined as functions measuring loss and gain when a given retrieval case solution is applied. Moreover, these functions help the user to choose the best solution so that when a mistake is made the resulting loss is minimal. In this way, the highest benefit is always obtained.


Fuzzy Sets and Systems | 2001

Use of a fuzzy machine learning technique in the knowledge acquisition process

Juan Luis Castro; Jose Jesus Castro-Schez; Jose Manuel Zurita

Acquiring the knowledge to support an expert system is one of the key activities in knowledge engineering. Knowledge acquisition (KA) is closely related to research in the machine learning field. Any machine learning acquires some knowledge, but not enough knowledge for building expert systems. The aim of this article is to present a new approach to machine learning which helps to acquire knowledge when building expert systems. This technique will acquire the more general knowledge that should be used for extending, updating and improving an incomplete and partially incorrect knowledge base (KB). The main claim of our approach is that the system will start with poor knowledge, provided by the expert or the organization to which he belongs. A machine learning technique will evolve it to an incomplete KB, which may be used for further interactions with the expert, that will incrementally extend and improve it until obtaining a complete KB (i.e., with complete inferential capabilities).


Fuzzy Sets and Systems | 1997

An inductive learning algorithm in fuzzy systems

Juan Luis Castro; Jose Manuel Zurita

Abstract The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy model. For this purpose, an ATMS shall be used. An algorithm obtaining the identification of the structure will be suggested. The minimal structure of the rule (with respect to the number of variables that must appear in the rule) will be found by this algorithm. Furthermore, the identification parameters shall be obtained simultaneously. The proposed method shall be applied for classification in an example. The Iris Plant Database shall be learnt for all three kinds of plants.


IEEE Transactions on Fuzzy Systems | 2004

FRIwE: fuzzy rule identification with exceptions

Pablo Carmona; Juan Luis Castro; Jose Manuel Zurita

In this paper, the FRIwE method is proposed to identify fuzzy models from examples. Such a method has been developed trying to achieve a double goal:accuracy and interpretability. In order to do that, maximal structure fuzzy rules are firstly obtained based on a method proposed by Castro et al. In a second stage, the conflicts generated by the maximal rules are solved, thus increasing the model accuracy. The resolution of conflicts are carried out by including exceptions in the rules. This strategy has been identified by psychologists with the learning mechanism employed by the human being, thus improving the model interpretability. Besides, in order to improve the interpretability even more, several methods are presented based on reducing and merging rules and exceptions in the model. The exhaustive use of the training examples gives the method a special suitability for problems with small training sets or high dimensionality. Finally, the method is applied to an example in order to analyze the achievement of the goals.


Knowledge Based Systems | 2011

Introducing attribute risk for retrieval in case-based reasoning

Juan Luis Castro; María Navarro; José Sánchez; Jose Manuel Zurita

One of the major assumptions in case-based reasoning is that similar experiences can guide future reasoning, problem solving and learning. This assumption shows the importance of the method used for choosing the most suitable case, especially when dealing with the class of problems in which risk, is relevant concept to the case retrieval process. This paper argues that traditional similarity assessment methods are not sufficient to obtain the best case; an additional step with new information must be performed necessary, after applying similarity measures in the retrieval stage. When a case is recovered from the case base, one must take into account not only the specific value of the attribute but also whether the case solution is suitable for solving the problem, depending on the risk produced in the final decision. We introduce this risk, as new information through a new concept called risk information that is entirely different from the weight of the attributes. Our article presents this concept locally and measures it for each attribute independently.


Fuzzy Sets and Systems | 1998

Non-monotomic fuzzy reasoning

Juan Luis Castro; Enric Trillas; Jose Manuel Zurita

Abstract Fuzzy reasoning can provide techniques both for representing and managing the imprecision in commonsense reasoning. But, like human reasoning, it conduces to inconsistencies (inherent to the imprecise or incomplete knowledge) that might be solved in the frame of fuzzy logic, simulating human behavior. In this paper we analyze this kind of conflicts and propose a non-monotonic fuzzy logic in order to solve it. Moreover, we show that many (non-monotonic) human reasoning patterns can be modeled by means of this “non-monotonic fuzzy reasoning”.


Expert Systems With Applications | 2010

A fuzzy expert system for business management

Daniel Arias-Aranda; Juan Luis Castro; María Navarro; José Sánchez; Jose Manuel Zurita

Nowadays firms are required to reach high levels of specialisation in order to increase their competitiveness in complex markets. Knowledge management plays a fundamental role in this process as the correct implementation of strategies is determined by the information transfer and dissemination within the organisation. In this paper, a fuzzy expert system focused on increasing accuracy and quality of the knowledge for decision making is designed. A model based on fuzzy rules to simulate the behavior of the firms, is presented under the assumption of determined input parameters previously detected and an algorithm is developed to achieve the minimal structure of the model. The result is a fuzzy expert system tool, called ESROM, which gives valuable information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be adapted to different scenarios.


Knowledge Based Systems | 2012

A high-performance FAQ retrieval method using minimal differentiator expressions

Alejandro Moreo; María Navarro; Juan Luis Castro; Jose Manuel Zurita

Case-Based Reasoning (CBR) has proven to be a very useful technique to solve problems in Closed-Domains Question Answering such as FAQ retrieval. Instead of trying to uderstand the question this method consists of retrieving the most similar case (Question/Answer pairs) among all cases by analogy. Keyword comparison criterion or statistical approaches are often used to implement similarity measure. However, those methods present the following disadvantages. On the one side, choosing keywords is an expert-knowledge domain-dependant task that is often performed manually. Furthermore, keyword comparison criterion does not guarantee the total differentiation among cases. On the other side, statistical approaches do not perform with enough information in sentence-level problems and are not interpretable. In order to alleviate these deficiencies we present a new method called the Minimal Differentiator Expressions (MDE) algorithm. This algorithm automatically obtains a set of linguistic patterns (expressions) used to retrieve the most relevant case to the user question. Those patterns present the following advantages: they are composed by the simplest sets of words which permit differentiation among cases and they are easily interpretable.


international conference hybrid intelligent systems | 2008

Using a CBR Approach Based on Ontologies for Recommendation and Reuse of Knowledge Sharing in Decision Making

José Luis Garrido; María Visitación Hurtado; Manuel Noguera; Jose Manuel Zurita

One of the possibilities for improving decision processes, and the knowledge management across interacting organizations is to explore successful past experiences. Case-based reasoning (CBR) is a problem solving strategy which is based on the reuse of past solutions (cases) to address new problems. Ontologies are a means to facilitate sharing and reuse of bodies of knowledge across organizations and applications on the basis of a well-defined and precise semantics for concepts and terms. This work presents a proposal aimed at knowledge reuse, during the decision activities by means of interwoven concepts from the knowledge management, CBR and ontologies research. This blended approach presents an ontological case construction for CBR systems as theoretical and empirical support for knowledge sharing. We obtain a formal characterization of a case by means of an ontological description of particular cases, and their interrelationships with cases stored in different case-repositories. An architecture for a distributed CBR system is proposed on the basis of a multiagent setting for semantic-based access to knowledge.

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Pablo Carmona

University of Extremadura

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José Sánchez

National University of Distance Education

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M. Romero

University of Granada

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Enric Trillas

Technical University of Madrid

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