Machine Learning Methods for Fuzzy Pattern Tree Induction

This thesis elaborates on a novel approach to fuzzy machine learning, that is, the combination of machine learning methods with mathematical tools for modeling and information processing based on fuzzy logic. More specifically, the thesis is devoted to so-called fuzzy pattern trees, a model class that has recently been introduced for representing dependencies between input and output variables in supervised learning tasks, such as classification and regression. Due to its hierarchical, modular structure and the use of different types of (nonlinear) aggregation operators, a fuzzy pattern tree has the ability to represent such dependencies in a very exible and compact way, thereby offering a reasonable balance between accuracy and model transparency. The focus of the thesis is on novel algorithms for pattern tree induction, i.e., for learning fuzzy pattern trees from observed data. In total, three new algorithms are introduced and compared to an existing method for the data-driven construction of pattern trees. While the first two algorithms are mainly geared toward an improvement of predictive accuracy, the last one focuses on eficiency aspects and seeks to make the learning process faster. The description and discussion of each algorithm is complemented with theoretical analyses and empirical studies in order to show the effectiveness of the proposed solutions

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification

Gini impurity PIs. (PDF 8 kb

Distributional regression for demand forecasting in e-grocery

Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management. Vol 09-2018. Bielefeld: Bielefeld University, Department of Business Administration and Economics; 2019.E-grocery offers customers an alternative to traditional brick-and-mortar grocery retailing. Customers select e-grocery for convenience, making use of the home delivery at a selected time slot. In contrast to brick-and-mortar retailing, in e-grocery on-stock information for stock keeping units (SKUs) becomes transparent to the customer before substantial shopping effort has been invested, thus reducing the personal cost of switching to another supplier. As a consequence, compared to brick-and-mortar retailing, on-stock availability of SKUs has a strong impact on the customer’s order decision, resulting in higher strategic service level targets for the e-grocery retailer. To account for these high service level targets, we propose a suitable model for accurately predicting the extreme right tail of the demand distribution, rather than providing point forecasts of its mean. Specifically, we propose the application of distributional regression methods— so-called Generalised Additive Models for Location, Scale and Shape (GAMLSS)—to arrive at the cost-minimising solution according to the newsvendor model. As benchmark models we consider linear regression, quantile regression, and some popular methods from machine learning. The models are evaluated in a case study, where we compare their out-of-sample predictive performance with regard to the service level selected by the e-grocery retailer considered

Multivariate modeling to identify patterns in clinical data: the example of chest pain

<p>Abstract</p> <p>Background</p> <p>In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.</p> <p>Methods</p> <p>We conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. A total of 1199 patients above age 35 presenting with chest pain were included in the study. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient's chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.</p> <p>Results</p> <p>A multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: „chest wall syndrome“, „vital threat“, „stomach and bowel pain“, „angina pectoris“, „chest infection syndrome“, and „ self-limiting chest pain“. MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.</p> <p>Conclusions</p> <p>Chest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level.</p

Exploring the interpersonal-, organization-, and system-level factors that influence the implementation and use of an innovation-synoptic reporting-in cancer care

<p>Abstract</p> <p>Background</p> <p>The dominant method of reporting findings from diagnostic and surgical procedures is the narrative report. In cancer care, this report inconsistently provides the information required to understand the cancer and make informed patient care decisions. Another method of reporting, the synoptic report, captures specific data items in a structured manner and contains only items critical for patient care. Research demonstrates that synoptic reports vastly improve the quality of reporting. However, synoptic reporting represents a complex innovation in cancer care, with implementation and use requiring fundamental shifts in physician behaviour and practice, and support from the organization and larger system. The objective of this study is to examine the key interpersonal, organizational, and system-level factors that influence the implementation and use of synoptic reporting in cancer care.</p> <p>Methods</p> <p>This study involves three initiatives in Nova Scotia, Canada, that have implemented synoptic reporting within their departments/programs. Case study methodology will be used to study these initiatives (the cases) in-depth, explore which factors were barriers or facilitators of implementation and use, examine relationships amongst factors, and uncover which factors appear to be similar and distinct across cases. The cases were selected as they converge and differ with respect to factors that are likely to influence the implementation and use of an innovation in practice. Data will be collected through in-depth interviews, document analysis, observation of training sessions, and examination/use of the synoptic reporting tools. An audit will be performed to determine/quantify use. Analysis will involve production of a case record/history for each case, in-depth analysis of each case, and cross-case analysis, where findings will be compared and contrasted across cases to develop theoretically informed, generalisable knowledge that can be applied to other settings/contexts. Ethical approval was granted for this study.</p> <p>Discussion</p> <p>This study will contribute to our knowledge base on the multi-level factors, and the relationships amongst factors in specific contexts, that influence implementation and use of innovations such as synoptic reporting in healthcare. Such knowledge is critical to improving our understanding of implementation processes in clinical settings, and to helping researchers, clinicians, and managers/administrators develop and implement ways to more effectively integrate innovations into routine clinical care.</p

Pattern trees for regression and fuzzy systems modeling

Fuzzy pattern tree induction has recently been introduced as a novel classification method in the context of machine learning. Roughly speaking, a pattern tree is a hierarchical, tree-like structure, whose inner nodes are marked with generalized (fuzzy) logical operators and whose leaf nodes are associated with fuzzy predicates on input attributes. In this paper, we adapt the method of pattern tree induction so as to make it applicable to another learning task, namely regression. Thus, instead of predicting one among a finite number of discrete class labels, we address the problem of predicting a real-valued target output. Apart from showing that fuzzy pattern trees are able to approximate real-valued functions in an accurate manner, we argue that such trees are also interesting from a modeling point of view. In fact, by describing a functional relationship between several input attributes and an output variable in an interpretable way, pattern trees constitute a viable alternative to classical fuzzy rule models. Compared to flat rule models, the hierarchical structure of patterns trees further allows for a more compact representation and for trading off accuracy against model simplicity in a seamless manner.