Fuzzy measures and integrals in re-identification problems

In this paper we give an overview of our approach of using aggregation operators, and more specifically, fuzzy integrals for solving re-identification problems. We show that the use of Choquet integrals are suitable for some kind of problems.Postprint (author’s final draft

Theory of Model Kohn-Sham Potentials and its Applications

The purpose of Kohn-Sham density functional theory is to develop increasingly accurate approximations to the exchange-correlation functional or to the corresponding potential. When one chooses to approximate the potential, the resulting model must be integrable, that is, a functional derivative of some density functional. Non-integrable potentials produce unphysical results such as energies that are not translationally or rotationally invariant. The thesis introduces methods for constructing integrable model potentials, developing properly invariant energy functionals from model potentials, and designing model potentials that yield accurate electronic excitation energies. Integrable potentials can be constructed using powerful analytic integrability conditions derived in this work. Alternatively, integrable potentials can be developed using the knowledge about the analytic structure of functional derivatives. When these two approaches are applied to the model potential of van Leeuwen and Baerends (which is non-integrable), they produce an exchange potential that has a parent functional and yields accurate energies. It is also shown that model potentials can be used to develop new energy functionals by the line-integration technique. When a model potential is not a functional derivative, the line integral depends on the choice of the integration path. By integrating the model potential of van Leeuwen and Baerends along the path of magnitude-scaled density, an accurate and properly invariant exchange functional is developed. Finally, a simple method to improve exchange-correlation potentials obtained from standard density-functional approximations is proposed. This method is based on the observation that an approximate Kohn-Sham potential of a fractionally ionized system is a better representation of the exact potential than the approximate Kohn-Sham potential of the corresponding neutral system. Removing 1/2 of an electron leads to the greatest improvement of the highest occupied molecular orbital energy, which explains why the Slater transition state method works well for predicting ionization energies. Removing about 1/4 of an electron improves orbital energy gaps and, when used in time-dependent density functional calculations, reduces errors of Rydberg excitation energies by almost an order of magnitude

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Measuring mental representations underlying activity-travel choices

The technological and societal challenges connected with the direct and indirect consequences of the still increasing traffic volume are keeping many people in research and practice busy. While some try to develop alternatives and travel demand measures to keep the traffic volume low others work on the improvement of travel demand prediction. Both have in common that they target human choice behaviour with their work. An essential condition for the success of travel demand measures and transport models is therefore to understand how individuals make their (travel) decisions and which needs they want to fulfill with their choices. The investigation of mental representations seems to be the key to understand human decision making. Mental representations are in fact images individuals bear in mind to oversee the consequences of their choices. They are tailored to the specific task and contextual setting under concern and show a significant simplification of reality. Next to the nature of the considered choice alternatives, the temporal construal of the task, the severeness of consequences and the (un)certainty of necessary information are held among others as determinants of mental representations. Shifts in the composition of mental representations are thus expectable when these contextual settings are changing. A drawback connected with the investigation of MRs is that so far only a few techniques exist by which these latent constructs can be elicited from individuals. Yet, all these methods are limited in the sense that they either influence or restrict respondents in their statements or are inappropriate for large-scale applications. This thesis introduced therefore a new online instrument for measuring mental representations which is able to collect data fully automatically. The first application of that instrument has its origin in the semi-structured CNET interview protocol from Arentze et al. (2008) and Dellaert et al. (2008). While online CNET is due to its open format still able to elicit an unbiased picture of respondents’ spontaneous recalls, adaptations to the original interview protocol had to be made to ensure the elicitation of benefits. In order to allow for a methodological comparison to online CNET an alternative application (online HL) has been developed that works only with revealed response options. As experimental subject a fictive trivial activity-travel choice task was chosen that consisted of scheduling working and grocery shopping activities for a normal working day in a fictive urban environment. In sum, decisions for the shopping location, the transport mode and the time of the shopping activity had to be considered. Side information was given for situational settings depending on the scenario. Next to the basic task four scenarios were developed of which one implied uncertainty about the side information, one implied a temporal distance of five years between the moment of decision making and the fictive moment of action, one introduced an additional online shopping alternative, and one implied negative consequences when the activity-travel task could not be fulfilled successfully. Data on these scenarios were collected among households subscribed to the nationwide Dutch LISS panel. The survey took place in two waves in spring and autumn 2010. While the first survey collected data on the basic, the uncertain and the distant scenario with both online CNET and HL, the second wave of experiments for the ecommerce and risky task was conducted with CNET only. In total, 1745 mental representations could be measured successfully which were subsequently analysed in an explorative and model-based approach. The analysis of the collected data showed a significant smaller structure of mental representations elicited with CNET compared to mental representations elicited with HL as the former consisted of significantly fewer components than the latter. This finding suggests an influence of the revealed handling of variables in HL which is supported by the fact that no shifts between scenarios could be measured with this technique. CNET however turned out to be sensitive for shifts caused by contextual manipulations. The substantial analysis of the uncertain, e-commerce and risky scenarios showed thus increased frequency and centrality values for attributes which were targeted by the experimental situations. For instance, the available product assortment nearly doubled its centrality value in the risky and uncertain scenario compared to the basic setting. Disappointing was however the distant scenario. An expected shift towards benefits could not be measured. A stable finding that was made with both techniques and in all scenarios was the high importance of the benefits time savings, ease of shopping and ease of travelling. These are in fact the driving forces of people’s choices for the investigated activity-travel task. These findings were supported by means of a formal model application which estimated parameters for MR component activation and strength of causal relationships in light of varying contexts. The analysis revealed that significant differences in MRs occur that result from situation-dependent need activation. The attributes on which choice alternatives are evaluated and the underlying benefits appear to be sensitive to (un)certainty of task-relevant information, the severeness of anticipated choice consequences and the set of choice alternatives. In conclusion, this thesis confirms the ability of online CNET to measure mental representations in a more sensitive and less influencing manner than online HL. Besides this scientific advantage CNET provides still all amenities of automatic online surveys for both respondents and researchers. These circumstances speak to the appropriateness of online CNET as a tool to elicit mental representations from decision makers of any choice task and perhaps also to a better understanding of human travel behaviour