An information theoretic approach to rule induction from databases

The knowledge acquisition bottleneck in obtaining rules directly from an expert is well known. Hence, the problem of automated rule acquisition from data is a well-motivated one, particularly for domains where a database of sample data exists. In this paper we introduce a novel algorithm for the induction of rules from examples. The algorithm is novel in the sense that it not only learns rules for a given concept (classification), but it simultaneously learns rules relating multiple concepts. This type of learning, known as generalized rule induction is considerably more general than existing algorithms which tend to be classification oriented. Initially we focus on the problem of determining a quantitative, well-defined rule preference measure. In particular, we propose a quantity called the J-measure as an information theoretic alternative to existing approaches. The J-measure quantifies the information content of a rule or a hypothesis. We will outline the information theoretic origins of this measure and examine its plausibility as a hypothesis preference measure. We then define the ITRULE algorithm which uses the newly proposed measure to learn a set of optimal rules from a set of data samples, and we conclude the paper with an analysis of experimental results on real-world data

See5 Algorithm versus Discriminant Analysis. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies

Prediction of insurance companies insolvency has arised as an important problem in the field of financial research, due to the necessity of protecting the general public whilst minimizing the costs associated to this problem. Most methods applied in the past to tackle this question are traditional statistical techniques which use financial ratios as explicative variables. However, these variables do not usually satisfy statistical assumptions, what complicates the application of the mentioned methods.In this paper, a comparative study of the performance of a well-known parametric statistical technique (Linear Discriminant Analysis) and a non-parametric machine learning technique (See5) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies upon the basis of a set of financial ratios. Results indicate a higher performance of the machine learning technique, what shows that this method can be a useful tool to evaluate insolvency of insurance firms.Insolvency, Insurance Companies, Discriminant Analysis, See5.

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

Asset selection using Factor Model and Data Envelope Analysis - A Quantile Regression approach

With the growing number of stocks and other financial instruments in the investment market, there is always a need for profitable methods of asset selection. The Fama-French three factor model, makes the problem of asset selection easy, by narrowing down the number of parameters, but the usual technique of Ordinary Least Square (OLS), used for estimation of the coefficients of the three factors suffers from the problem of modelling using the conditional mean of the distribution, as is the case with OLS. In this paper, we use the technique of Data Envelopment Analysis (DEA) applied to the Fama-French Three Factor Model, to choose stocks from Dow Jones Industrial Index. We use a more robust technique called as Quantile Regression to estimate the coefficients for the factor model and show that the assets selected using this regression method form a higher return equally weighted portfolio.Asset Selection, Factor Model, DEA, Quantile Regression

Rethinking feasibility analysis for urban development: a multidimensional decision support tool

Large-scale urban development projects featured over the past thirty years have shown some critical issues related to the implementation phase. Con-sequently, the current practice seems oriented toward minimal and wide-spread interventions meant as urban catalyst. This planning practice might solve the problem of limited reliability of large developments’ feasibility studies, but it rises an evaluation demand related to the selection of coali-tion of projects within a multidimensional and multi-stakeholders deci-sion-making context. This study aims to propose a framework for the generation of coalitions of elementary actions in the context of urban regeneration processes and for their evaluation using a Multi Criteria Decision Analysis approach. The proposed evaluation framework supports decision makers in exploring dif-ferent combinations of actions in the context of urban interventions taking into account synergies, i.e. positive or negative effects on the overall per-formance of an alternative linked to the joint realization of specific pairs of actions. The proposed evaluation framework has been tested on a pilot case study dealing with urban regeneration processes in the city of Milan (Italy)