Multi-objective evolutionary algorithms for feature selection : application in bankruptcy prediction

A Multi-Objective Evolutionary Algorithm (MOEA) was adapted in order to deal with problems of feature selection in datamining. The aim is to maximize the accuracy of the classifier and/or to minimize the errors produced while minimizing the number of features necessary. A Support Vector Machines (SVM) classifier was adopted. Simultaneously, the parameters required by the classifier were also optimized. The validity of the methodology proposed was tested in the problem of bankruptcy prediction using a database containing financial statements of 1200 medium sized private French companies. The results produced shown that MOEA is an efficient feature selection approach and the best results were obtained when the accuracy, the errors and the classifiers parameters are optimized.The financial support of the Portuguese science foundation (FCT) under grant PTDC/GES/70168/2006 is acknowledged

기계학습의 변수선별법을 통한 금융데이터 분석

학위논문 (석사)-- 서울대학교 대학원 : 수리과학부, 2015. 2. 최형인.Global financial crisis has been occurred frequently in these days also na- tions and companies pay attention to predict bankruptcy. In this thesis, we discuss feature selection method to extract feature group that causes main factors of making bankruptcy. We describe stepwise method and principal component analysis method briefl y and compare it to construct prediction model. In addition, we try to analyze their performance and statistical mea- surement which method is the most efficient to raw data. We deal with data set of experiments which consist of 515 companies' financial statement in 1997 to build the model by using support vector machine.Abstract i 1 Introduction 1 2 Feature Selection Method 3 2.1 Stepwise method . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Principal component analysis . . . . . . . . . . . . . . . . . . 5 3 Data and experiment 10 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4 Result 14 4.1 Stepwise method . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2 Principal component analysis . . . . . . . . . . . . . . . . . . 15 4.3 Comparing feature selection . . . . . . . . . . . . . . . . . . . 16 5 Conclusion 20 Abstract (in Korean) 22Maste

Feature selection for bankruptcy prediction: a multi-objective optimization approach

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – was fully analyzed using two classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously, the parameters required by both classifiers were also optimized. The validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The method proposed can provide useful information for the decision maker in characterizing the financial health of a company

Self-adaptive MOEA feature selection for classification of bankruptcy prediction data

Article ID 314728Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved.This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy).The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.This work was partially supported by the Portuguese Foundation for Science and Technology under Grant PEst-C/CTM/LA0025/2011 (Strategic Project-LA 25-2011-2012) and by the Spanish Ministerio de Ciencia e Innovacion, under the project "Gestion de movilidad efficiente y sostenible, MOVES" with Grant Reference TIN2011-28336

Semantic Data Pre-Processing for Machine Learning Based Bankruptcy Prediction Computational Model

This paper studies a Bankruptcy Prediction Computational Model (BPCM model) – a comprehensive methodology of evaluating companies’ bankruptcy level, which combines storing, structuring and pre-processing of raw financial data using semantic methods with machine learning analysis techniques. Raw financial data are interconnected, diverse, often potentially inconsistent, and open to duplication. The main goal of our research is to develop data pre-processing techniques, where ontologies play a central role. We show how ontologies are used to extract and integrate information from different sources, prepare data for further processing, and enable communication in natural language. Using ontology, we give meaning to the disparate and raw business data, build logical relationships between data in various formats and sources and establish relevant context. Our Ontology of Bankruptcy Prediction (OBP Ontology) which provides a conceptual framework for companies’ financial analysis, is built in the widely established Prote ́ge ́ environment. An OBP Ontology can be effectively described with a graph database. Graph database expands the capabilities of traditional databases tackling the interconnected nature of economic data and providing graph-based structures to store information allowing the effective selection of the most relevant input features for the machine learning algorithm. To create and manage the BPCM Graph Database (Graph DB), we use the Neo4j environment and Neo4j query language, Cypher, to perform feature selection of the structured data. Selected key features are used for the Machine Learning Engine – supervised MLP Neural Network with Sigmoid activation function. The programming of this component is performed in Python. We illustrate the approach and advantages of semantic data pre-processing applying it to a representative use case