Neural RELAGGS

Multi-relational databases are the basis of most consolidated data collections in science and industry today. Most learning and mining algorithms, however, require data to be represented in a propositional form. While there is a variety of specialized machine learning algorithms that can operate directly on multi-relational data sets, propositionalization algorithms transform multi-relational databases into propositional data sets, thereby allowing the application of traditional machine learning and data mining algorithms without their modification. One prominent propositionalization algorithm is RELAGGS by Krogel and Wrobel, which transforms the data by nested aggregations. We propose a new neural network based algorithm in the spirit of RELAGGS that employs trainable composite aggregate functions instead of the static aggregate functions used in the original approach. In this way, we can jointly train the propositionalization with the prediction model, or, alternatively, use the learned aggegrations as embeddings in other algorithms. We demonstrate the increased predictive performance by comparing N-RELAGGS with RELAGGS and multiple other state-of-the-art algorithms.Comment: Submitted to Machine Learning Journa

Using ILP to Identify Pathway Activation Patterns in Systems Biology

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist

Black Men\u27s Responsible Fatherhood Narratives: Fatherhood, Responsibility, Race, and Gender

Over the last few decades increasing rates of single mother households in the United States have triggered a national alarm over the effects of father absence on society. Father absence has been linked specifically to many of the problems plaguing black communities in the United States (e.g. poverty, low educational attainment, etc.) and as a result community and political leaders alike have consistently promoted responsible fatherhood practices as a way to address them. Although responsible fatherhood has received, in this context, a considerable amount of social attention, this attention has come intertwined with considerable political and moral rhetoric at all levels, making an idea invested with a wide variety of often-conflicting meanings and interests. Given the paucity of academic studies giving voice to black fathers at the metaphoric front line of the national responsible fatherhood effort, this author used a variation of The Listening Guide (Gilligan 2003) to capture the narratives of four black fathers volunteering in a local responsible fatherhood program. Critical Social Representations Theory was used to frame the interaction between participants and the social contexts within which they are embedded, paying particular attention to participants\u27 positioning in regard to social representations of race and gender. The widely different understandings of fatherhood present within the results point to fatherhood as a highly dynamic concept. Responsibility, on the other hand, was understood primarily as father presence, a middle class ideal that I argue is problematic given the realities of poor black fathers. Finally, all fathers tended to resist ideas of race as essence, even if in regard to gender all fathers adopted hegemonic positions endorsing views of gender difference as essential and as grounded in biology. Overall, results reveal complex portrayals of black fathers and their lives in communities where race, poverty, incarceration, drugs, violence, or family court all pose additional challenges to responsible fatherhood

Visualizing multidimensional data similarities:Improvements and applications

Multidimensional data is increasingly more prominent and important in many application domains. Such data typically consist of a large set of elements, each of which described by several measurements (dimensions). During the design of techniques and tools to process this data, a key component is to gather insights into their structure and patterns, which can be described by the notion of similarity between elements. Among these techniques, multidimensional projections and similarity trees can effectively capture similarity patterns and handle a large number of data elements and dimensions. However, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we propose methods that make the computation of similarity trees efficient for large datasets, and also enhance its visual representation to allow the exploration of more data in a limited screen. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of groups of similar elements. These are automatically annotated to describe which dimensions are more important to define their notion of group similarity. We show next how these explanatory mechanisms can be adapted to handle both static and time-dependent data. Our proposed techniques are designed to be easy to use, work nearly automatically, and are demonstrated on a variety of real-world large data obtained from image collections, text archives, scientific measurements, and software engineering