Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Case-based reasoning (CBR) is a unique tool for the evaluation of possible failure of firms (EOPFOF) for its eases of interpretation and implementation. Ensemble computing, a variation of group decision in society, provides a potential means of improving predictive performance of CBR-based EOPFOF. This research aims to integrate bagging and proportion case-basing with CBR to generate a method of proportion bagging CBR for EOPFOF. Diverse multiple case bases are first produced by multiple case-basing, in which a volume parameter is introduced to control the size of each case base. Then, the classic case retrieval algorithm is implemented to generate diverse member CBR predictors. Majority voting, the most frequently used mechanism in ensemble computing, is finally used to aggregate outputs of member CBR predictors in order to produce final prediction of the CBR ensemble. In an empirical experiment, we statistically validated the results of the CBR ensemble from multiple case bases by comparing them with those of multivariate discriminant analysis, logistic regression, classic CBR, the best member CBR predictor and bagging CBR ensemble. The results from Chinese EOPFOF prior to 3 years indicate that the new CBR ensemble, which significantly improved CBRs predictive ability, outperformed all the comparative methods

EGAL: Exploration Guided Active Learning for TCBR

The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present a simple but effective exploration only selection strategy for AL in the textual domain. Our approach is inherently case-based, using only nearest-neighbour-based density and diversity measures. We show how its performance is comparable to the more computationally expensive exploitation-based approaches and that it offers the opportunity to be classifier independent

Framework for data quality in knowledge discovery tasks

Actualmente la explosión de datos es tendencia en el universo digital debido a los avances en las tecnologías de la información. En este sentido, el descubrimiento de conocimiento y la minería de datos han ganado mayor importancia debido a la gran cantidad de datos disponibles. Para un exitoso proceso de descubrimiento de conocimiento, es necesario preparar los datos. Expertos afirman que la fase de preprocesamiento de datos toma entre un 50% a 70% del tiempo de un proceso de descubrimiento de conocimiento. Herramientas software basadas en populares metodologías para el descubrimiento de conocimiento ofrecen algoritmos para el preprocesamiento de los datos. Según el cuadrante mágico de Gartner de 2018 para ciencia de datos y plataformas de aprendizaje automático, KNIME, RapidMiner, SAS, Alteryx, y H20.ai son las mejores herramientas para el desucrimiento del conocimiento. Estas herramientas proporcionan diversas técnicas que facilitan la evaluación del conjunto de datos, sin embargo carecen de un proceso orientado al usuario que permita abordar los problemas en la calidad de datos. Adem´as, la selección de las técnicas adecuadas para la limpieza de datos es un problema para usuarios inexpertos, ya que estos no tienen claro cuales son los métodos más confiables. De esta forma, la presente tesis doctoral se enfoca en abordar los problemas antes mencionados mediante: (i) Un marco conceptual que ofrezca un proceso guiado para abordar los problemas de calidad en los datos en tareas de descubrimiento de conocimiento, (ii) un sistema de razonamiento basado en casos que recomiende los algoritmos adecuados para la limpieza de datos y (iii) una ontología que representa el conocimiento de los problemas de calidad en los datos y los algoritmos de limpieza de datos. Adicionalmente, esta ontología contribuye en la representacion formal de los casos y en la fase de adaptación, del sistema de razonamiento basado en casos.The creation and consumption of data continue to grow by leaps and bounds. Due to advances in Information and Communication Technologies (ICT), today the data explosion in the digital universe is a new trend. The Knowledge Discovery in Databases (KDD) gain importance due the abundance of data. For a successful process of knowledge discovery is necessary to make a data treatment. The experts affirm that preprocessing phase take the 50% to 70% of the total time of knowledge discovery process. Software tools based on Knowledge Discovery Methodologies offers algorithms for data preprocessing. According to Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, KNIME, RapidMiner, SAS, Alteryx and H20.ai are the leader tools for knowledge discovery. These software tools provide different techniques and they facilitate the evaluation of data analysis, however, these software tools lack any kind of guidance as to which techniques can or should be used in which contexts. Consequently, the use of suitable data cleaning techniques is a headache for inexpert users. They have no idea which methods can be confidently used and often resort to trial and error. This thesis presents three contributions to address the mentioned problems: (i) A conceptual framework to provide the user a guidance to address data quality issues in knowledge discovery tasks, (ii) a Case-based reasoning system to recommend the suitable algorithms for data cleaning, and (iii) an Ontology that represent the knowledge in data quality issues and data cleaning methods. Also, this ontology supports the case-based reasoning system for case representation and reuse phase.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Fernando Fernández Rebollo.- Secretario: Gustavo Adolfo Ramírez.- Vocal: Juan Pedro Caraça-Valente Hernánde

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models\u27 predictions. Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a database of 2258 cases mixed from multiple institutions. The generalization of the models was tested on second set of 2177 cases. Clusters were identified in the database using a priori knowledge and unsupervised learning methods (agglomerative hierarchical clustering followed by K-Means, SOM, AutoClass). The performance of the global models over the clusters was examined and local models were trained for clusters. While some local models were superior to some global models, we were unable to build a modular CAD system that was better than the global BP-ANN model. The ensemble systems based on simplistic combination schemes did not result in significant improvements and more complicated combination schemes were found to be unduly optimistic. One of the most striking results of this dissertation was that CAD systems trained on a mixture of lesion types performed much better on masses than on calcifications. Our study of the institutional effects suggests that models built on cases mixed between institutions may overcome some of the weaknesses of models built on cases from a single institution. It was suggestive that each of the unsupervised methods identified a cluster of younger women with well-circumscribed or obscured, oval-shaped masses that accounted for the majority of the BP-ANN’s recommendations for follow up. From the cluster analysis and the CART models, we determined a simple diagnostic rule that performed comparably to the global BP-ANN. Approximately 98% sensitivity could be maintained while providing approximately 26% specificity. This should be compared to the clinical status quo of 100% sensitivity and 0% specificity on this database of indeterminate cases already referred to biopsy

A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks

Recently, advances in Information Technologies (social networks, mobile applications, Internet of Things, etc.) generate a deluge of digital data; but to convert these data into useful information for business decisions is a growing challenge. Exploiting the massive amount of data through knowledge discovery (KD) process includes identifying valid, novel, potentially useful and understandable patterns from a huge volume of data. However, to prepare the data is a non-trivial refinement task that requires technical expertise in methods and algorithms for data cleaning. Consequently, the use of a suitable data analysis technique is a headache for inexpert users. To address these problems, we propose a case-based reasoning system (CBR) to recommend data cleaning algorithms for classification and regression tasks. In our approach, we represent the problem space by the meta-features of the dataset, its attributes, and the target variable. The solution space contains the algorithms of data cleaning used for each dataset. We represent the cases through a Data Cleaning Ontology. The case retrieval mechanism is composed of a filter and similarity phases. In the first phase, we defined two filter approaches based on clustering and quartile analysis. These filters retrieve a reduced number of relevant cases. The second phase computes a ranking of the retrieved cases by filter approaches, and it scores a similarity between a new case and the retrieved cases. The retrieval mechanism proposed was evaluated through a set of judges. The panel of judges scores the similarity between a query case against all cases of the case-base (ground truth). The results of the retrieval mechanism reach an average precision on judges ranking of 94.5% in top 3, for top 7 84.55%, while in top 10 78.35%.The authors are grateful to the research groups: Control Learning Systems Optimization Group (CAOS) of the Carlos III University of Madrid and Telematics Engineering Group (GIT) of the University of Cauca for the technical support. In addition, the authors are grateful to COLCIENCIAS for PhD scholarship granted to PhD. David Camilo Corrales. This work has been also supported by: Project Alternativas Innovadoras de Agricultura Inteligente para sistemas productivos agrícolas del departamento del Cauca soportado en entornos de IoT financed by Convocatoria 04C-2018 Banco de Proyectos Conjuntos UEES-Sostenibilidad of Project Red de formación de talento humano para la innovación social y productiva en el Departamento del Cauca InnovAcción Cauca, ID-3848. The Spanish Ministry of Economy, Industry and Competitiveness (Projects TRA2015-63708-R and TRA2016-78886-C3-1-R)

Case-based retrieval framework for gene expression data

© the authors, publisher and licensee Libertas academica Limited. Background: The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process.Methods: This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. Results: The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children’s Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. Conclusion: The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps