Rosalía Laza

Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study

The parameter setting of an algorithm that will result in optimal performance differs across problem instance domains. Users spend a lot of time tuning algorithms for their specific problem domain, and this time could be saved by an automatic approach for parameter tuning. In this paper, we present a system that recommends the parameter configuration of an algorithm that solves a problem, conditioned by the particular features of the current problem instance to be solved. The proposed system is based on a basic adjustment model designed by authors (Pavon, R., Diaz, F., & Luzon, V. (2008). A model for parameter setting based on Bayesian networks. Engineering Applications of Artificial Intelligence, 21(1), 14-25) in which starting from experimental results concerning the search for solutions to several instances of the problem, a Bayesian network (BN) is induced and tries to infer the best configuration for the algorithm used. However, taking into account that the optimal parameter configuration may differ considerably across problem instances of a specific domain, the present work extends the former incorporating additional information about problem instances and using the case-based reasoning (CBR) methodology as the framework integrator for the different instances from the same problem, where each problem instance deals with a specific BN. In this way, the system will automatically recommend a parameter configuration for a given algorithm according to the characteristics of the problem instance at hand and past experience of similar instances. As an example, we empirically evaluate our Bayesian CBR system to tune a genetic algorithm for solving the root identification problem. The experimental results demonstrate the validity of the model proposed.

A Reasoning Model for CBR_BDI Agents Using an Adaptable Fuzzy Inference System

This paper proposes to automate the generation of shellfish exploitation plans, which are elaborated by Galician extracting entities. For achieving this objective a CBR-BDI agent will be used. This agent will adapt the exploitation plans to the environmental characteristics of each school of shellfish. This kind of agents develops its activity into changing and dynamic environments, so the reasoning model that they include must be emphasised. The agent reasoning model is guided by the phases of the CBR life cycle, using different technologies for each phase. The use of an adaptative neuro-fuzzy inference system in the reuse phase must be highlighted.

INCREASING THE AUTONOMY OF DELIBERATIVE AGENTS WITH A CASE-BASED REASONING SYSTEM

This paper shows how deliberative agents can be built by means of a case-based reasoning system. The concept of deliberative agent is introduced and the case-based reasoning model is presented. Once the advantages and disadvantages of such agents have been discussed, it will be shown how to solve some of their inconveniences, especially those related to their implementation and adaptation. The World Wide Web has emerged as one of the most popular vehicle for disseminating and sharing information through computer networks; a distributed agent-based solution for e-business, in which such agents have been used, is also presented and evaluated in this paper.

Evaluating the effect of unbalanced data in biomedical document classification.

Nowadays, document classification has become an interesting research field. Partly, this is due to the increasing availability of biomedical information in digital form which is necessary to catalogue and organize. In this context, machine learning techniques are usually applied to text classification by using a general inductive process that automatically builds a text classifier from a set of pre-classified documents. Related with this domain, imbalanced data is a well-known problem in many practical applications of knowledge discovery and its effects on the performance of standard classifiers are remarkable. In this paper, we investigate the application of a Bayesian Network (BN) model for the triage of documents, which are represented by the association of different MeSH terms. Our results show that BNs are adequate for describing conditional independencies between MeSH terms and that MeSH ontology is a valuable resource for representing Medline documents at different abstraction levels. Moreover, we perform an extensive experimental evaluation to investigate if the classification of Medline documents using a BN classifier poses additional challenges when dealing with class-imbalanced prediction. The evaluation involves two methods, under-sampling and cost-sensitive learning. We conclude that BN classifier is sensitive to both balancing strategies and existing techniques can improve its overall performance.

Automatic parameter tuning for Evolutionary Algorithms using a Bayesian Case-Based Reasoning system

The widespread use and applicability of Evolutionary Algorithms is due in part to the ability to adapt them to a particular problem-solving context by tuning their parameters. This is one of the problems that a user faces when applying an Evolutionary Algorithm to solve a given problem. Before running the algorithm, the user typically has to specify values for a number of parameters, such as population size, selection rate, and probability operators. This paper empirically assesses the performance of an automatic parameter tuning system in order to avoid the problems of time requirements and the interaction of parameters. The system, based on Bayesian Networks and Case-Based Reasoning methodology, estimates the best parameter setting for maximizing the performance of Evolutionary Algorithms. The algorithms are applied to solve a basic problem in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The experimental results demonstrate the validity of the proposed system and its potential effectiveness for configuring algorithms.

Using the eServices platform for detecting behavior patterns deviation in the elderly assisted living: a case study.

Worlds aging population is rising and the elderly are increasingly isolated socially and geographically. As a consequence, in many situations, they need assistance that is not granted in time. In this paper, we present a solution that follows the CRISP-DM methodology to detect the elderlys behavior pattern deviations that may indicate possible risk situations. To obtain these patterns, many variables are aggregated to ensure the alert system reliability and minimize eventual false positive alert situations. These variables comprehend information provided by body area network (BAN), by environment sensors, and also by the elderlys interaction in a service provider platform, called eServices—Elderly Support Service Platform. eServices is a scalable platform aggregating a service ecosystem developed specially for elderly people. This pattern recognition will further activate the adequate response. With the system evolution, it will learn to predict potential danger situations for a specified user, acting preventively and ensuring the elderlys safety and well-being. As the eServices platform is still in development, synthetic data, based on real data sample and empiric knowledge, is being used to populate the initial dataset. The presented work is a proof of concept of knowledge extraction using the eServices platform information. Regardless of not using real data, this work proves to be an asset, achieving a good performance in preventing alert situations.

genEnsemble: A new model for the combination of classifiers and integration of biological knowledge applied to genomic data

In the last years, microarray technology has become widely used in relevant biomedical areas such as drug target identification, pharmacogenomics or clinical research. However, the necessary prerequisites for the development of valuable translational microarray-based diagnostic tools are (i) a solid understanding of the relative strengths and weaknesses of underlying classification methods and (ii) a biologically plausible and understandable behaviour of such models from a biological point of view. In this paper we propose a novel classifier able to combine the advantages of ensemble approaches with the benefits obtained from the true integration of biological knowledge in the classification process of different microarray samples. The aim of the current work is to guarantee the robustness of the proposed classification model when applied to several microarray data in an inter-dataset scenario. The comparative experimental results demonstrated that our proposal working with biological knowledge outperforms other well-known simple classifiers and ensemble alternatives in binary and multiclass cancer prediction problems using publicly available data.

A dynamic model for integrating simple web spam classification techniques

Techniques and heuristics applied for spam dissemination.Rule-based classification model for web spam detection.Knowledge actualization based on incremental learning.Filter performance improvement by classifier fusion.WSF2 framework publicly available under LGPL license. Over the last years, Internet spam content has spread enormously inside web sites mainly due to the emergence of new web technologies oriented towards the online sharing of resources and information. In such a situation, both academia and industry have shown their concern to accurately detect and effectively control web spam, resulting in a good number of anti-spam techniques currently available. However, the successful integration of different algorithms for web spam classification is still a challenge. In this context, the present study introduces WSF2, a novel web spam filtering framework specifically designed to take advantage of multiple classification schemes and algorithms. In detail, our approach encodes the life cycle of a case-based reasoning system, being able to use appropriate knowledge and dynamically adjust different parameters to ensure continuous improvement in filtering precision with the passage of time. In order to correctly evaluate the effectiveness of the dynamic model, we designed a set of experiments involving a publicly available corpus, as well as different simple well-known classifiers and ensemble approaches. The results revealed that WSF2 performed well, being able to take advantage of each classifier and to achieve a better performance when compared to other alternatives. WSF2 is an open-source project licensed under the terms of the LGPL publicly available at https://sourceforge.net/projects/wsf2c/.

Assessing the Impact of Class-Imbalanced Data for Classifying Relevant/Irrelevant Medline Documents

Imbalanced data is a well-known common problem in many practical applications of machine learning and its effects on the performance of standard classifiers are remarkable. In this paper we investigate if the classification of Medline documents using MeSH controlled vocabulary poses additional challenges when dealing with class-imbalanced prediction. For this task, we evaluate the performance of Bayesian networks by using some available strategies to overcome the effect of class imbalance. Our results show both that Bayesian network classifiers are sensitive to class imbalance and existing techniques can improve their overall performance.

Agent-based web engineering

Technological evolution of the Internet world is fast and constant. Successful systems should have the capacity to adapt to it and should be provided with mechanisms that allow them to decide what to do according to such changes. This paper shows how an autonomous intelligent agent can be used to develop web-based systems with the requirements of today users. Internet applications should be reactive, proactive, and autonomous and have to be capable of adapting to changes in its environment and in the user behavior. The technological proposal presented in the paper also facilitates the interoperability and scalability of distributed systems.