Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains

There has been increased interest in devising learning techniques that combine unlabeled data with labeled data ? i.e. semi-supervised learning. However, to the best of our knowledge, no study has been performed across various techniques and different types and amounts of labeled and unlabeled data. Moreover, most of the published work on semi-supervised learning techniques assumes that the labeled and unlabeled data come from the same distribution. It is possible for the labeling process to be associated with a selection bias such that the distributions of data points in the labeled and unlabeled sets are different. Not correcting for such bias can result in biased function approximation with potentially poor performance. In this paper, we present an empirical study of various semi-supervised learning techniques on a variety of datasets. We attempt to answer various questions such as the effect of independence or relevance amongst features, the effect of the size of the labeled and unlabeled sets and the effect of noise. We also investigate the impact of sample-selection bias on the semi-supervised learning techniques under study and implement a bivariate probit technique particularly designed to correct for such bias

Cost-Effective Classification for Credit Decision-Making

There is an increasing need for credit decision making systems that can dynamically analyze historical data and learn complex relations among the most important attributes for loan evaluation. In this paper we propose the application of a new machine learning algorithm, QLC, to the credit analysis of consumer loans. The algorithm learns how to classify a loan by minimizing the expected cost due to both credit investigation expenses and possible misclassification. QLC is built upon reinforcement learning. A dataset of actual consumer loans issued for evaluating the algorithm. The experiments reported show that QLC performs better than other cost-sensitive algorithms on this dataset.Il y a un besoin toujours croissant de syst\ue8mes de prise de d\ue9cision en mati\ue8re de cr\ue9dit qui puissent analyser de fa\ue7on dynamique les donn\ue9es historiques et apprendre les relations complexes entre les attributs les plus importants d'\ue9valuation de pr\ueat. Dans cet article, nous proposons l'application d'un nouvel algorithme d'apprentissage machine, soit QLC, pour analyser le cr\ue9dit dans le cas de pr\ueats au consommateur. L'algorithme apprend comment classifier un pr\ueat en minimisant le co\ufbt pr\ue9vu d\ufb \ue0 la fois aux d\ue9penses de l'enqu\ueate sur le cr\ue9dit et \ue0 une erreur possible de classification. QLC se fonde sur l'apprentissage par renforcement. Un ensemble de donn\ue9es de pr\ueats r\ue9els sert \ue0 \ue9valuer l'algorithme. Les exp\ue9riences cit\ue9es montrent que QLC a une meilleure performance que d'autres algorithmes sensibles au co\ufbt dans cet ensemble de donn\ue9es.NRC publication: Ye

Q-Learning: Literature Review State of the Art Research Approaches

NRC publication: Ye

ARTIFICIAL INTELLIGENCE APPROACHES TO DIAGNOSIS AND PLANNING OF ECONOMIC SYSTEMS BASED ON DECISION MAKING MODELS

THE GENERIC TASKS OF DIAGNOSIS AND PLANNING HAVE LONG BEEN STIMULATING RESEARCHIN THE ARTIFICIAL INTELLIGENCE (AT) COMMUNITY FOR THE CONSTRUCTION OF PROBLEM-SOLVING METHODS AND KNOWLEDGE-BASE ARCHITECTURES. IT IS THE PURPOSE OF THIS THESIS TO DEFINE THE AI TASKS OF DIAGNOSIS AND PLANNING IN ECONOMIC PROBLEM -SOLVING AND TO DEVELOP REASONING METHODS WHICH IN COLLABORATION WITH THE DECISION-MAKER CAN INFER ON THESE TAKS. A "DEEP" MODELLING FORMALISM IS PROPOSED THAT INTEGRATES QUALITATIVE AND QUANTITATIVE MODELLING. A DIAGNOSTIC METHOD IS DEVELOPEDWHICH USES THE "DEEP" MODEL FOR GENERATING AND TESTING HYPOTHESES ABOUT THE BEHAVIOUR OF AN ECONOMIC SYSTEM. FOR THE PLANNING TASK, THE DESIGN OF AN INTELLIGENT AGENT IS EXAMINED . IN INTERACTION WITH THE DECISION-MAKER AND A STOCHASTICSIMULATION MODEL, THE PROPOSED HYBRID REINFORCEMENT LEARNING AGENT LEARNS THE POLICY WHICH IS OPTIMAL ACCORDING TO THE PREFERENCES OF THE DECISION- MAKER.ΟΙ ΠΡΩΤΟΓΕΝΕΙΣ ΔΙΕΡΓΑΣΙΕΣ ΤΗΣ ΔΙΑΓΝΩΣΗΣ ΚΑΙ ΤΟΥ ΣΧΕΔΙΑΣΜΟΥ ΕΧΟΥΝ ΕΚ ΜΑΚΡΟΥ ΠΑΡΑΚΙΝΗΣΕΙ ΕΡΕΥΝΑ ΣΤΟ ΧΩΡΟ ΤΗΣ ΤΕΧΝΗΤΗΣ ΝΟΗΜΟΣΥΝΗΣ ΓΙΑ ΤΗΝ ΚΑΤΑΣΚΕΥΗ ΜΕΘΟΔΩΝ ΕΠΙΛΥΣΗΣ ΠΡΟΒΛΗΜΑΤΩΝ ΚΑΙ ΑΡΧΙΤΕΚΤΟΝΙΚΩΝ ΒΑΣΕΩΝ ΓΝΩΣΗΣ ΚΥΡΙΩΣ ΣΕ ΠΕΔΙΑ ΕΦΑΡΜΟΓΩΝ ΜΗΧΑΝΙΚΗΣ (ENGINEERING). ΣΚΟΠΟΣ ΤΗΣ ΔΙΑΤΡΙΒΗΣ ΕΙΝΑΙ ΝΑ ΟΡΙΣΕΙ ΤΙΣ ΔΙΕΡΓΑΣΙΕΣ ΤΗΣ ΚΑΙ ΤΟΥ ΣΧΕΔΙΑΣΜΟΥ ΟΙΚΟΝΟΜΙΚΩΝ ΣΥΣΤΗΜΑΤΩΝ ΚΑΙ ΝΑ ΑΝΑΠΤΥΞΕΙ ΜΕΘΟΔΟΥΣ ΟΙ ΟΠΟΙΕΣ ΣΕ ΣΥΝΕΡΓΑΣΙΑ ΜΕ ΤΟΝ ΑΝΘΡΩΠΟ ΑΝΑΛΑΜΒΑΝΟΥΝ ΤΗΝ ΕΚΤΕΛΕΣΗ ΤΩΝ ΔΥΟ ΔΙΕΡΓΑΣΙΩΝ. ΠΡΟΤΕΙΝΕΤΑΙ Η ΟΛΟΚΛΗΡΩΣΗ ΠΟΙΟΤΙΚΗΣ ΚΑΙ ΠΟΣΟΤΙΚΗΣ ΥΠΟΔΕΙΓΜΑΤΟΠΟΙΗΣΗΣ ΜΕ ΤΗΝ ΑΝΑΠΑΡΑΣΤΑΣΗ ΕΝΟΣ ΟΙΚΟΝΟΜΙΚΟΥ ΣΥΣΤΗΜΑΤΟΣ ΜΕ "ΒΑΘΥ" ΥΠΟΔΕΙΓΜΑ. ΜΕ ΤΗ ΧΡΗΣΗ ΤΟΥ ΥΠΟΔΕΙΓΜΑΤΟΣΑΥΤΟΥ ΥΛΟΠΟΙΕΙΤΑΙ ΕΥΡΙΣΤΙΚΗ ΜΕΘΟΔΟΣ ΔΙΑΓΝΩΣΤΙΚΗΣ ΣΥΛΛΟΓΙΣΤΙΚΗΣ ΟΙΚΟΝΟΜΙΚΟΥ ΣΥΣΤΗΜΑΤΟΣ. ΓΙΑ ΤΟ ΣΧΕΔΙΑΣΜΟ ΚΑΤΑΣΚΕΥΑΖΕΤΑΙ ΕΥΦΥΗΣ ΟΝΤΟΤΗΤΑ Η ΟΠΟΙΑ ΣΕ ΣΥΝΕΡΓΑΣΙΑ ΜΕ ΤΟΝ ΑΝΘΡΩΠΟ ΚΑΙ ΤΟ ΣΤΟΧΑΣΤΙΚΟ ΥΠΟΔΕΙΓΜΑ ΤΟΥ ΣΥΣΤΗΜΑΤΟΣ ΜΑΘΑΙΝΕΙ ΜΕ ΜΑΘΗΣΗ ΜΕΕΝΙΣΧΥΣΗ ΤΗΝ ΠΟΛΙΤΙΚΗ ΠΟΥ ΕΙΝΑΙ ΒΕΛΤΙΣΤΗ ΣΥΜΦΩΝΑ ΜΕ ΤΙΣ ΠΡΟΤΙΜΗΣΕΙΣ ΤΟΥ ΧΡΗΣΤΗ

Cost-Effective Classification for Credit Decision Making Knowledge

There is an increasing need for credit decision making systems that can dynamically analyze historical data and learn complex relations among the most important attributes for loan evaluation. In this paper we propose the application of a new machine learning algorithm, QLC, to the credit analysis of consumer loans. The algorithm learns how to classify a loan by minimizing the expected cost due to both credit investigation expenses and possible misclassification. QLC is built upon reinforcement learning. A dataset of actual consumer loans is used for evaluating the algorithm. The experiments reported show that QLC performs better than other cost-sensitive algorithms on this dataset. 1. Introduction According to a recent U.S. Banker survey amongst the 113 top U.S. banks [15], the most popular approaches for automated decision-making for all types of credit products are application scoring and on-line credit bureau scoring. These credit-scoring procedures refer to the evaluation of each..