Eduardo Lupiani

Populating Knowledge Based Decision Support Systems

Knowledge-based decision support systems KBDSS hold up business and organizational decision-making activities on the basis of the knowledge available concerning the domain under question. One of the main problems with knowledge bases is that their construction is a time-consuming task. A number of methodologies have been proposed in the context of the Semantic Web to assist in the development of ontology-based knowledge bases. In this paper, we present a technique for populating knowledge bases from semi-structured text which take advantage of the semantic underpinnings provided by ontologies. This technique has been tested and evaluated in the financial domain

Evaluating Case-Base Maintenance algorithms

The success of a Case-Based Reasoning (CBR) system closely depends on its knowledge-base, named the case-base. The life cycle of CBR systems usually implies updating the case-base with new cases. However, it also implies removing useless cases for reasons of efficiency. This process is known as Case-Base Maintenance (CBM) and, in recent decades, great efforts have been made to automatise this process using different kind of algorithms (deterministic and non-deterministic). Indeed, CBR system designers find it difficult to choose from the wealth of algorithms available to maintain the case-base. Despite the importance of such a key decision, little attention has been paid to evaluating these algorithms. Although classical validation methods have been used, such as Cross-Validation and Hold-Out, they are not always valid for non-deterministic algorithms. In this work, we analyse this problem from a methodological point of view, providing an exhaustive review of these evaluation methods supported by experimentation. We also propose a specific methodology for evaluating Case-Base Maintenance algorithms (the @a@b evaluation). Experiment results show that this method is the most suitable for evaluating most of the algorithms and datasets studied.

A Multi-Objective Evolutionary Algorithm Fitness Function for Case-Base Maintenance

Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a multi-objective optimization problem to achieve both goals. Multi-Objective Evolutionary Algorithms (MOEAs) have been recognised as appropriate techniques for multi-objective optimisation because they perform a search for multiple solutions in parallel. In the present paper we introduce a fitness function based on the Complexity Profiling model to perform CBM with MOEA, and we compare its results against other known CBM approaches. From the experimental results, CBM with MOEA shows regularly good results in many case-bases, despite the amount of redundant and noisy cases, and with a significant potential for improvement.

Monitoring elderly people at home with temporal Case-Based Reasoning

Abstract This paper presents a study of why and how Case-Based Reasoning (CBR) can be used in the long term to help elderly people living alone in a Smart Home. The work focuses on the need to manage the temporal dimension and how the system must be maintained. The proposal involves the integration of a CBR system in a commercial Smart Home architecture that includes sensors, data communication and data integration. The CBR system analyses the daily activity at home as temporal event sequences, using temporal edit distance to identify the most similar cases. Most common Case-Based Maintenance (CBM) algorithms adapted to the temporal problem (t-CNN, t-RENN, t-ICF, t-DROP1 and t-RCFP) are used to reduce the number of cases in the case base in order to contribute to its long term maintenance. The experiments carried out analyse the effect of different temporal CBM algorithms in common risk scenarios (waking up during the night, falls and falls with loss of consciousness). Data experiments are generated synthetically based on real behaviour patterns of 12 hours’ and 24 hours’ duration. Algorithms are compared using a paired t-test analysis. The results show that the algorithms t-CNN and t-DROP1 are able to create case-bases that statistically present the same average results as the original case-base but with a 10–20% in size. Algorithms t-ICF, t-RCFP and t-RENN can build similar case-bases to the original with a 10–50% size reduction, although they are not totally equivalent since they have significantly different average results than the original case-base. Finally, algorithm t-RENN does not significantly reduce the size of the case-base because it commonly deletes cases describing abnormal scenarios. We demonstrate that the proposed temporal CBR system is able to detect the different proposed risk scenarios when there is a large number of cases. That is, the CBR systems are useful in the long term. Experiments indicate that the temporal CBM algorithms analysed are able to reduce case-bases successfully to detect abnormal scenarios. However, success in creating a maintained case-base equivalent to the original depends on the number of cases.

Case-base maintenance with multi-objective evolutionary algorithms

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels. In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the Case-Based Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.

A Proposal of Temporal Case-Base Maintenance Algorithms

Time plays a key role in describing stories and cases. In the last decades, great efforts have been done to develop Case-Based Reasoning (CBR) systems that can cope with the temporal dimension. Despite this kind of system finds it difficult to maintain their case-base, little attention has been paid to maintain temporal case-bases. Case-Base Maintenance (CBM) algorithms are useful tools to build efficient and reliable CBR systems. In this work, we propose the extension of different CBM approaches to deal with this problem. Five temporal CBM algorithms are proposed (t-CNN, t-RENN, t-DROP1, t-ICF and t-RC-FP). These algorithms make the maintenance of case-bases possible when cases are temporal event sequences.

Using case-based reasoning to detect risk scenarios of elderly people living alone at home

In today’s ageing societies, the proportion of elderly people living alone in their own homes is dramatically increasing. Smart homes provide the appropriate environment for keeping them independent and, therefore, enhancing their quality of life. One of the most important requirements of these systems is that they have to provide a pervasive environment without disrupting elderly people’s daily activities. The present paper introduces a CBR agent used within a commercial Smart Home system, designed for detecting domestic accidents that may lead to serious complications if the elderly resident is not attended quickly. The approach is based on cases composed of event sequences. Each event sequence represents the different locations visited by the resident during his/her daily activities. Using this approach, the system can decide whether the current sequence represent an unsafe scenario or not. It does so by comparing the current sequence with previously stored sequences. Several experiments have been conducted with different CBR agent configurations in order to test this approach. Results from these experiments show that the proposed approach is able to detect unsafe scenarios.

Evaluating case selection algorithms for analogical reasoning systems

An essential issue for developing analogical reasoning systems (such as Case-Based Reasoning systems) is to build the case memory by selecting registers from an external database. This issue is called case selection and the literature provides a wealth of algorithms to deal with it. For any particular domain, to choose the case selection algorithm is a critical decision on the system design. Despite some algorithms obtain good results, a specific algorithms evaluation is needed. Most of the efforts done in this line focus on the number of registers selected and providing a simple evaluation of the system obtained. In some domains, however, the system must fulfil certain constraints related to accuracy or efficiency. For instance, in the medical field, specificity and sensitivity are critical values for some tests. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. In order to demonstrate the usefulness of this methodology, we present new case selection algorithms based on evolutionary multi-objective optimization. We compare the classical algorithms and the multi-objective approach in order to select the most suitable case selection algorithm according to different standard problems.

An evolutionary multiobjective constrained optimisation approach for case selection: evaluation in a medical problem

A solid building process and a good evaluation of the knowledge base are essential in the clinical application of Case-Based Reasoning Systems. Unlike other approaches, each piece of the knowledge base (cases of the case memory) is knowledge-complete and independent from the rest. Therefore, the main issue to build a case memory is to select which cases must be included or removed. Literature provides a wealth of methods based on instance selection from a database. However, it can be also understood as a multiobjective problem, maximising the accuracy of the system and minimising the number of cases in the case memory. Most of the efforts done in this evaluation of case selection methods focus on the number of registers selected, providing an evaluation of the system based on its accuracy. On the one hand, some case selection methods follow a non deterministic approach. Therefore, a rough evaluation could entail to inaccurate conclusions. On the other hand, specificity and sensitivity are critical values to evaluate tests in the medical field. However, these parameters are hardly ever included in the case selection evaluation. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. We also propose a case selection method based on multiobjective constrained optimisation for which Evolutionary Algorithms are used. Finally, we illustrate the use of this methodology by evaluating classic and the case selection method proposed, in a particular problem of Burn Intensive Care Units.