Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning

Knowledge representation and diagnostic inference using Bayesian networks in the medical discourse

For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. This includes both generic and experience-based specific knowledge, which is stored in a knowledge base. For knowledge processing, a combination of the problem-solving methods of concept-based and case-based reasoning is used. Concept-based reasoning is used for the diagnosis, therapy and medication recommendation and evaluation of generic knowledge. Exceptions in the form of specific patient cases are processed by case-based reasoning. In addition, the use of Bayesian networks allows to deal with uncertainty, fuzziness and incompleteness. Thus, the valid general concepts can be issued according to their probability. To this end, various inference mechanisms are introduced and subsequently evaluated within the context of a developed prototype. Tests are employed to assess the classification of diagnoses by the network

A case-based reasoning approach to intelligent retrieval in reusable software libraries

Software Reuse is the technique of reusing software components previously developed in order to reduce the effort required to develop new software. It is generally accepted that software reuse can improve the rate of software development, reduce the costs and increase reliability, However, software reuse is only effective if it is easier to locate and adapt a reusable software component than to write it from scratch. There are many issues and problems that need to be resolved before the benefits of software reuse become widespread. These involve philosophical issues such as the encapsulation of experience and the formation of organizational structures to support it, and also technical issues such as the identification, classification, retrieval and adaptation of reusable components. This paper is concerned with the automated retrieval of software components from a reusable software library using case-based reasoning. Current automated retrieval is generally adapted from conventional text retrieval methods which are based on matching lexical and semantic attributes of software components. While these methods are easily implemented, they have serious limitations that arise out of the fact that the words and phrases used to describe software components and their functions are usually obscure, ambiguous and imprecise. Case-based reasoning is an artificial intelligence technique that makes use of a stored set of previously solved problems (cases) in order to solve new ones. It is an effective method of applying the experience and problem solving knowledge gained in the past to bear on current problems. Case-based systems find solutions to problems by examining an input situation or problem and searching a case base to find a case or situation that matches its characteristic features. If the match is identical, the problem is solved. Case-based reasoning shells are inexpensive and their retrieval mechanisms are complex. This together with the fact that the attribute based classification of cases can be used to classify reusable components, affords the opportunity of efficient intelligent retrieval of reusable components. This paper shows how both attribute and faceted based classification schemes can be accommodated by a case-based reasoning shell and examines various methods of retrieval

Assessing confidence in cased based reuse step

Case-Based Reasoning (CBR) is a learning approach that solves current situations by reusing previous solutions that are stored in a case base. In the CBR cycle the reuse step plays an important role into the problem solving process, since the solution for a new problem is based in the available solutions of the retrieved cases. In classification tasks a trivial reuse method is commonly used, which takes into account the most frequently solution proposed by the set of retrieved cases. We propose an alternative reuse process; we call confidence-reuse method, which make a qualitative assessment of the information retrieved. This approach is focused on measuring the solution accuracy, applying some confidence predictors based in a k-NN classifier with the aim of analyzing and evaluating the information offered by the retrieved cases.Peer Reviewe

Strong, Fuzzy and Smooth Hierarchical Classification for Case-Based Problem Solving

Colloque avec actes et comité de lecture. internationale.International audienceThis paper explains how case-based problem solving can have benefit from a hierarchical organisation of problems based on a generality relation. Three adaptation-guided retrieval processes are described. The strong classification in a problem hierarchy is a classical deductive process. It is based on the generality relation between problems which organises the hierarchy. The fuzzy classification is a fuzzification of the strong classification. It is based on a fuzzy generality relation between problems, which can be seen as a non-symmetrical similarity measure. The smooth classification extends the fuzzy classification: it is also based on a similarity or dissimilarity measure but takes into account problem and solution adaptation knowledge. These processes have been successfully implemented in two case-based reasoning systems: Resyn/CBR in the domain of organic synthesis and Kasimir/CBR in the domain of cancer treatment

A new approach for web usage mining using case based reasoning

In this study, we present a new approach for Web Usage Mining using Case Based Reasoning. Case-Based Reasoning techniques are a knowledge-based problem-solving approach which is based on the reuse of previous work experience. Thus, the past experience can be deemed as an efficient guide for solving new problems. Web personalization systems which have the capability to adapt the next set of visited pages to individual users according to their interests and navigational behaviors have been proposed. The proposed architecture consists of a number of components, namely, basic log preprocessing, pattern discovery methods (By Case Based Reasoning and peer to peer similarity—Clustering—association rules mining methods), and recommendations. One of the issues considered in this study is that there are no recommendations to those who are different from the existing users in the log file. Also, it is one of the challenges facing the recommendations systems. To deal with this problem, Apriori algorithm was designed individually in order to be utilized in presenting recommendations; in other words, in cases where recommendations may be inadequate, using association rules can enhance the overall system performance recommendations. A new method used in this study is clustering algorithms for Nominal web data. Our evaluations show that the proposed method along with Standard case-classified Log provides more effective recommendations for the users than the Logs with no case classification

Complexity modelling for case knowledge maintenance in case-based reasoning.

Case-based reasoning solves new problems by re-using the solutions of previously solved similar problems and is popular because many of the knowledge engineering demands of conventional knowledge-based systems are removed. The content of the case knowledge container is critical to the performance of case-based classification systems. However, the knowledge engineer is given little support in the selection of suitable techniques to maintain and monitor the case base. This research investigates the coverage, competence and problem-solving capacity of case knowledge with the aim of developing techniques to model and maintain the case base. We present a novel technique that creates a model of the case base by measuring the uncertainty in local areas of the problem space based on the local mix of solutions present. The model provides an insight into the structure of a case base by means of a complexity profile that can assist maintenance decision-making and provide a benchmark to assess future changes to the case base. The distribution of cases in the case base is critical to the performance of a case-based reasoning system. We argue that classification boundaries represent important regions of the problem space and develop two complexity-guided algorithms which use boundary identification techniques to actively discover cases close to boundaries. We introduce a complexity-guided redundancy reduction algorithm which uses a case complexity threshold to retain cases close to boundaries and delete cases that form single class clusters. The algorithm offers control over the balance between maintaining competence and reducing case base size. The performance of a case-based reasoning system relies on the integrity of its case base but in real life applications the available data invariably contains erroneous, noisy cases. Automated removal of these noisy cases can improve system accuracy. In addition, error rates can often be reduced by removing cases to give smoother decision boundaries between classes. We show that the optimal level of boundary smoothing is domain dependent and, therefore, our approach to error reduction reacts to the characteristics of the domain by setting an appropriate level of smoothing. We introduce a novel algorithm which identifies and removes both noisy and boundary cases with the aid of a local distance ratio. A prototype interface has been developed that shows how the modelling and maintenance approaches can be used in practice in an interactive manner. The interface allows the knowledge engineer to make informed maintenance choices without the need for extensive evaluation effort while, at the same time, retaining control over the process. One of the strengths of our approach is in applying a consistent, integrated method to case base maintenance to provide a transparent process that gives a degree of explanation

A case-based meta-learning and reasoning framework for classifiers selection

© 2018 ACM. In machine learning area, a large number of classification algorithms are available that can be used for solving the problems of prediction and classification in different domains. These classifiers perform differently on different learning problems. For example, if one algorithm perform better on one dataset, the same algorithm may perform badly on another dataset. The reason is that each dataset has its own nature in terms of its local and global characteristics. Similarly, the number of candidate algorithms are also large in number and is therefore very hard for a machine learning practitioner to know the intrinsic behaviors of the algorithms on different kinds of datasets and are therefore unable to select a right algorithm for his problem in-hand. To overcome the issue, this study proposes an automatic classifier selection methodology. A case-based meta-learning and reasoning (CB-MLR) framework is designed and implemented to recommend appropriate classifier for mining the new dataset. The framework exploits inherit characteristics of the datasets mapped against the algorithms performance. The key contributions of CB-MLR include: (a) design of a flexible and incremental meta-learning and reasoning framework using multiview learning, and (b) implementation of the CBR methodology to accurately recommend most relevant top-3 classifiers as the suggested algorithms for the new data mining problem. The proposed framework is tested for 9 decision tree classifiers, from Weka environment, and 52 datasets from UCI repository over a case-base of 100 resolved cases. The accuracy obtained is 94% within the scope of top-3 most relevant classifiers

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering