مقاله انگلیسی رایگان در مورد تنظیم اطلاعات فردی با روش های خوشه بندی – الزویر 2019

abstract

The evaluation of subjective data is a very demanding task. The classification of the information gathered from human evaluators and the possible high noise levels introduced are ones of the most difficult issues to deal with. This situation leads to adopt individuals who can be considered as experts in the specific application domain. Thus, the development of Expert Systems (ES) that consider the opinion of these individuals have been appeared to mitigate the problem. In this work an original methodology for the selection of subjective sequential data for the training of ES is presented. The system is based on the arrangement of knowledge acquired from a group of human experts. An original similarity measure between the subjective evaluations is proposed. Homogeneous groups of experts are produced using this similarity through a clustering algorithm. The methodology was applied to a practical case of the Intelligent Transportation Systems (ITS) domain for the training of ES for driving risk prediction. The results confirm the relevance of selecting homogeneous information (grouping similar opinions) when generating a ground truth (a reliable signal) for the training of ES. Further, the results show the need of considering subjective sequential data when working with phenomena where a set of rules could not be easily learned from human experts, such as risk assessment.

Introduction

The practice of Knowledge Engineering (Van Do, Le Thi, & Nguyen, 2018) has become a very useful approach to solve complex problems that require a high level of human expertise. This discipline involves integrating knowledge into computer systems which emulates the decision-making ability of a human expert in a specific domain. The systems in charge of achieving these tasks are the Expert Systems (ES) (Agarwal & Goel, 2014). The building, maintaining and development of ES (Djamal et al., 2017) are mainly based on the interaction between the knowledge engineer and the domain expert (Yau & Sattar, 1994). The devel- opment of a reliable ES requires a deep understanding and a good representation of the knowledge of the domain expert. In most of the cases, the knowledge representation is based on a set of rules (a production system) that ease the explanation of the decision-making made by the inference engine (Wick & Slagle, 1989). These rules are build from the knowledge acquired from human experts with the application of Machine Learning techniques (such as Neural Networks (Lin & Zhang, 2012), Deep Learning (Wei, He, Chen, Zhou, & Tang, 2017), Decision Trees (Sriram & Yuan, 2012), Fuzzy Logic (Wang, Lee, & Ho, 2007), Bayesian methods (WenBin, XiaoLing, YiJun, & Yu, 2010), Genetic Algorithms (Daza et al., 2011), among others). Knowledge acquisition is a process which aims to extract knowledge, experience and problem-solving procedures from one or more domain experts. Several techniques have been proposed for a correct knowledge acquisition (see Hua, 2008 for a complete review). Nevertheless, there are several problems that must be considered when acquiring knowledge from human experts (Gaines, 1987): • Experts may not be able to express their knowledge in a structured way • Experts may not be aware of the significance of the knowledge they have used. • The expressed knowledge may be irrelevant, incomplete or not understandable. In some cases, depending on the field of application, it may be easier to extract the knowledge from human experts through a continuous scale. This is the case of the risk assessment, where the knowledge could be acquired in a predefined scale (e.g. from 0, no risk, to 100, maximum risk). Here, the knowledge of the experts is gathered in form of subjective sequential data (Prelec, 2004) and could be treated as time series for its study and integration (see, for instance, de Diego, Crespo, Siordia, Conde, & Cabello, 2011; de Diego, Siordia, Conde, & Cabello, 2011; Siordia, de Diego, Conde, & Cabello, 2011a). However, the integration of several opinions into a unique ground truth (i.e. a reliable signal) is a hard-to-achieve task (Liou & Nunamaker, 1990). Two different scenarios appear. The consideration of knowledge from too few experts could provide a ground truth with insufficient information. In contrast, the consideration of knowledge from too many experts could generate a noisy ground truth due to the appearance of possible contradictions between their evaluations (Turban, 1991). Different statistical approaches have been proposed in the past (see, for instance, meta-analysis methods in Brockwell & Gordon (2001)).