Robust techniques and applications in fuzzy clustering

This dissertation addresses issues central to frizzy classification. The issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which is a collective cluster of all data points but views outliers and good points differently (as opposed to the concept of Dave\u27s Noise cluster). The scheme is presented and validated with experiments and similarities with Noise Clustering (NC) are also presented. The other scheme is based on the feasible solution algorithm that implements the Least Trimmed Squares (LTS) estimator. The LTS estimator is known to be resistant to noise and has a high breakdown point. The feasible solution approach also guarantees convergence of the solution set to a global optima. Experiments show the practicability of the proposed schemes in terms of computational requirements and in the attractiveness of their simplistic frameworks. The issue of validation of clustering results has often received less attention than clustering itself. Fuzzy and non-fuzzy cluster validation schemes are reviewed and a novel methodology for cluster validity using a test for random position hypothesis is developed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by the partitioning algorithm. The Hopkins statistic is used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here. A unique feature selection procedure for use with large molecular conformational datasets with high dimensionality is also developed. The intelligent feature extraction scheme not only helps in reducing dimensionality of the feature space but also helps in eliminating contentious issues such as the ones associated with labeling of symmetric atoms in the molecule. The feature vector is converted to a proximity matrix, and is used as an input to the relational fuzzy clustering (FRC) algorithm with very promising results. Results are also validated using several cluster validity measures from literature. Another application of fuzzy clustering considered here is image segmentation. Image analysis on extremely noisy images is carried out as a precursor to the development of an automated real time condition state monitoring system for underground pipelines. A two-stage FCM with intelligent feature selection is implemented as the segmentation procedure and results on a test image are presented. A conceptual framework for automated condition state assessment is also developed

Fuzzy Set Methods for Object Recognition in Space Applications

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms

The Development of Attitudes Toward Scientific Models During a Participatory Modeling Process – The Impact of Participation and Social Network Structure

Scientific models are increasingly being used to support participatory natural resources management decision making processes. These models allow stakeholders and scientists to explore potential policy and management options and can help facilitate discussion surrounding concerning uncertainty and different sources of knowledge. The unique benefits of participatory modeling processes, however, are contingent upon stakeholders understanding of, engagement with, and willingness to use the scientific models as sources of knowledge and information. Little is known, however, about how stakeholders view scientific models within these processes. We examined changes in stakeholders’ attitudes toward scientific models over the course of OysterFutures, a 2-year, facilitated participatory modeling process that aimed to create consensus recommendations for oyster management in the Choptank River Complex, MD, United States. Five ordered logistic regression models were used to test hypotheses concerning the impact of social network measures, factors related to the participatory modeling process itself, and stakeholder characteristics on salience, credibility and legitimacy (SCL) attitudes toward models. Results suggested that stakeholders’ ways of knowing was a significant driver of salience, credibility and legitimacy elements of attitudes toward models. Additionally, acting as a gatekeeper within the social network resulted in significantly lower attitudes toward model credibility. These results indicate that the scientific model acted as a boundary object that facilitated discussion during the participatory modeling process. By better understanding the factors that influence model attitude formation, these processes can adjust their design and function to better take advantage of these models. Additionally, practitioners can have more realistic expectations concerning the role of models within participatory, collaborative natural resources decision-making processes

Opportunities and Challenges in Commissioning Materiality-Driven Sustainability Reporting Towards the SDGs: The Case of Cadeler A/S

Frequently and recently tightening and expanding sustainability reporting policies and requirements can pose significant administrative burdens on SMEs upholding a strong culture of accountability to their stakeholder network. This seminal case study examines how a Danish offshore wind farm commissioner can efficiently (1) navigate towards credibility in and (2) derive actionable insights from their sustainability (reporting) integration trajectory by capitalizing on the increasingly emphasized materiality principle. Group-based Fuzzy AHP and Textual Analysis aim to excavate and assess senior managers’ and external stakeholders’ preferences based on the GRI Standards and the UN’s SDG targets. Internal priorities emphasize safety, compliance, and profitability, whereas external stakeholders’ and their groups’ priorities exhibit mixed findings on their type and extent of alignment with the former. Content elements assigned higher relative importance tend to be more robust to changes in decision-makers’ uncertainty and verbal bias. The author confirms that a simplicity-informativeness trade-off tends to be driven by stakeholder grouping and that a data-driven, subject-based, and objectifying approach should be complemented with context, managerial judgment, and process iteration. Keywords: Sustainability; materiality; prioritization; credibility; actionability.Frequently and recently tightening and expanding sustainability reporting policies and requirements can pose significant administrative burdens on SMEs upholding a strong culture of accountability to their stakeholder network. This seminal case study examines how a Danish offshore wind farm commissioner can efficiently (1) navigate towards credibility in and (2) derive actionable insights from their sustainability (reporting) integration trajectory by capitalizing on the increasingly emphasized materiality principle. Group-based Fuzzy AHP and Textual Analysis aim to excavate and assess senior managers’ and external stakeholders’ preferences based on the GRI Standards and the UN’s SDG targets. Internal priorities emphasize safety, compliance, and profitability, whereas external stakeholders’ and their groups’ priorities exhibit mixed findings on their type and extent of alignment with the former. Content elements assigned higher relative importance tend to be more robust to changes in decision-makers’ uncertainty and verbal bias. The author confirms that a simplicity-informativeness trade-off tends to be driven by stakeholder grouping and that a data-driven, subject-based, and objectifying approach should be complemented with context, managerial judgment, and process iteration. Keywords: Sustainability; materiality; prioritization; credibility; actionability

A Hybrid Analytic Hierarchy Process and Likert Scale Approach for the Quality Assessment of Medical Education Programs

The quality assessment of training courses is of utmost importance in the medical education field to improve the quality of the training. This work proposes a hybrid multicriteria decision-making approach based on two methodologies, a Likert scale (LS) and the analytic hierarchy process (AHP), for the quality assessment of medical education programs. On one hand, the qualitative LS method was adopted to estimate the degree of consensus on specific topics; on the other hand, the quantitative AHP technique was employed to prioritize parameters involved in complex decision-making problems. The approach was validated in a real scenario for evaluating healthcare training activities carried out at the Centre of Biotechnology of the National Hospital A.O.R.N. “A. Cardarelli” of Naples (Italy). The rational combination of the two methodologies proved to be a promising decision-making tool for decision makers to identify those aspects of a medical education program characterized by a lower user satisfaction degree (revealed by the LS) and a higher priority degree (revealed by the AHP), potentially suggesting strategies to increase the quality of the service provided and to reduce the waste of resources. The results show how this hybrid approach can provide decision makers with helpful information to select the most important characteristics of the delivered education program and to possibly improve the weakest ones, thus enhancing the whole quality of the training courses

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

Flow-based reputation: more than just ranking

The last years have seen a growing interest in collaborative systems like electronic marketplaces and P2P file sharing systems where people are intended to interact with other people. Those systems, however, are subject to security and operational risks because of their open and distributed nature. Reputation systems provide a mechanism to reduce such risks by building trust relationships among entities and identifying malicious entities. A popular reputation model is the so called flow-based model. Most existing reputation systems based on such a model provide only a ranking, without absolute reputation values; this makes it difficult to determine whether entities are actually trustworthy or untrustworthy. In addition, those systems ignore a significant part of the available information; as a consequence, reputation values may not be accurate. In this paper, we present a flow-based reputation metric that gives absolute values instead of merely a ranking. Our metric makes use of all the available information. We study, both analytically and numerically, the properties of the proposed metric and the effect of attacks on reputation values