![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/08AF5882-6F35-4806-B21F-AA972D21227A | - |
| Identifier | http://users.ics.forth.gr/~potamias/home_page/p3.pdf | - |
| Language | en | - |
| Title | Inductive learning support for decision making | en |
| Creator | Moustakis Vasilis | en |
| Creator | Μουστακης Βασιλης | el |
| Creator | George Potamias | en |
| Content Summary | In this paper we review the applicability of representative inductive machine learning approaches in multicriteria decision making. We limit our review to four systems. We use SICLA and KBG as representative conceptual clustering systems and ID3 and CN2 as representative learning from examples systems. We demonstrate our results by way of two real world decision making exemplars. The first exemplar concerns the evaluation of retail outlets [15]. The second exemplar concerns venture capital assessment [16]. We discuss the conditions under which inductive learning methodologies can be effectively implemented to support decision making. Inductive machine learning was pioneered by Michalski [9]. It aims at the derivation of knowledge from a set of observations, or facts. In cases where facts are known to belong to a certain class we speak of concept acquisition or learning from examples. In such an instance we target our inquiry towards the derivation of concept identification rules. Rules may be either discriminant or characteristic. When concept classes underlying fact membership are not known we speak of learning from observations, or conceptual clustering. Accordingly, we look forward toward the partitioning of facts into a meaningful and disjoint set of clusters. A cluster represents a “coming together in space and time so that the density of whatever is clustered contrasts with the density around” [6, p.33]. Generalization and specialization are essential processes when making inductive inferences. The basic premise characterizing any inductive inference is falsity preservation. The derivation of a hypothesis H from facts E is falsity preserving in the sense that “if some facts falsify E, then they must also falsify H” [9, p.89]. Although inductive machine learning is a rather new field there are several and successful ‘fielded’ applications [7, 8]. Carter and Catlett [2] propose a methodology for credit card assessment using inductive learning techniques. Also, Shaw and Gentry [14] present an approach for company risk assessment that is based on inductive learning. Both applications are exploratory; they, however, stress the potential of inductive learning in decision making support. We maintain that learning is a trait of decision making: “quite often the decision maker is interested in finding out what his weights are or what they should be under different decision circumstances. In this sense, the weights of importance could be considered as desirable outputs rather than independent inputs of an analysis. Weights must be revealed or learned through a careful interactive process”, [17, p.22] - emphasis is ours. In this paper we discuss the methodological issues underlying the application of inductive learning techniques in business decision making. We limit our endeavor to four representative and well-known inductive learning systems, ID3 [12, 11, 13], CN2 [5, 4], KBG [1] and SICLA [3]. These systems are part of the Machine Learning Toolbox [7, 18]. We explore inductive system suitability by way of three decision making exemplars. We draw our exemplars from retail outlet evaluation and venture capital assessment. We target our inquiry toward the evaluation of pros and cons, concerning the application of the selected inductive learning systems, in real world business decision making. Specifically, our research focuses around the following lines: 1. Grouping of alternatives into disjoint cluster groups. We use a Lexicographic Evaluation Functional, LEF, criterion to optimize clustering [10]. 2. Identification of the most significant criteria for either alternative discrimination or alternative characterization. Suppose that we have two alternative courses of action, a1 and a2. We are interested in differences between a1 and a2, or in what a1 and a2 are all about. Furthermore, we present a methodology for inducing criteria weights. 3. Identification of relevant and accurate discrimination and recognition rules. We associate this line with the previous one. 4. Identification of the most representative alternative for each decision class. We steer our venture in the direction of deriving a conceptual indexing scheme for alternative courses of action. 5. Identification of bias and error resulting from contextual factors. We define context to represent the decision making environment. Furthermore, we explore the implications of our research in decision making. We place emphasis upon the expert critiquing and case based reasoning paradigms. | en |
| Type of Item | Σύντομη Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Short Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-07 | - |
| Date of Publication | 1993 | - |
| Bibliographic Citation | V. Moustakis, G. Potamias. (1993). Inductive learning support for decision making. Presented at 3rd International Workshop on Artificial Intelligence in Economics and Management. [Online]. Available: http://users.ics.forth.gr/~potamias/home_page/p3.pdf | en |
Copyright © DIAS 2013
