A System for Induction of Oblique Decision Trees

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

The History and Evolution of North American Ski Resort Map Style and Design

The first official ski resort in North America opened to the public in 1936 in Sun Valley, Idaho. Today, there are over 400 resorts across the continent in constant competition with one another for customers. Ski maps were first introduced as navigational tools, but quickly became a means for advertising what the resorts had to offer. The desire to outshine rival resorts has produced thousands of unique ski maps over the years, forming a collection of maps that has never been comprehensively analyzed until now. The first phase of the thesis involved the gathering of historical and modern ski maps to be examined and catalogued into a database. A total of 1,779 maps were archived along with relevant attribute information concerning the style and design of each map. This database, in addition to interviews with key informants and historical texts, helps to provide a complete picture of North American ski mapping history for the first time. The second phase of the thesis was a quantitative survey of skiers and snowboarders to assess users’ impressions and satisfaction levels of modern ski maps. Several examples of ski maps were presented to the survey respondents, and they were asked to provide their opinions and concerns about the overall appearance of the map. It was found that most respondents preferred the traditional painted panoramic style over other styles of printed ski maps, and many were hesitant about the effectiveness of newer mobile ski mapping alternatives. While it appears that printed maps are around to stay for now, the recent retirement of prominent ski map painter James Niehues may signify the demise of traditionally hand-painted ski resort maps. Extensive literature already exists regarding the reproduction of hand-painted panoramas using computer software programs, and as the digital image becomes more accepted in our culture it is possible that many future ski resort maps will be digitally-rendered

Automated flight planning for roof inspection using a face-based approach

The rapid proliferation of consumer small unmanned aerial systems (sUASs) has expanded ownership to include amateurs and professionals alike. These platforms in combination with numerous open source and proprietary applications tailored to gather aerial imagery and generate 3D point clouds and meshes from aerial imagery, have made 3D modeling available to anyone who can afford an entry-level sUAS. These flight plans force the sensor to remain at greater distances from their targets, resulting in varying spatial resolution of sloped surfaces. The work described here explains the development of a variety of 3D automated flight plans to provide vantage points not achievable by constant-altitude, nadir-looking imagery. Specifically, the issue of roof inspection is addressed in detail. This work generates an automated flight plan that positions the sUAS and orients its sensor such that the focal plane array is parallel to the roof plane based on a priori knowledge of the roof\u27s geometry, greatly reducing single- or two-point perspective. This a priori knowledge can come from a variety sources including databases, a site survey, or data extracted from an existing point cloud. Still images or video from orthogonal flight plans can be used for visual inspection, or the generation of dense point clouds and meshes. These products are compared to those generated from nadir imagery. This novel flight planning approach permits the aircraft to fly the orthogonal flight plans from start to finish without intervention from the remote pilot. This work is scalable to similar sUAS-based tasks including aerial-based thermography of buildings and infrastructure

Automatic classification of web images as UML static diagrams using machine learning techniques

Our purpose in this research is to develop a method to automatically and efficiently classify web images as Unified Modeling Language (UML) static diagrams, and to produce a computer tool that implements this function. The tool receives a bitmap file (in different formats) as an input and communicates whether the image corresponds to a diagram. For pragmatic reasons, we restricted ourselves to the simplest kinds of diagrams that are more useful for automated software reuse: computer-edited 2D representations of static diagrams. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19,000 web images manually classified by experts. In this work, we do not consider the textual contents of the images. Our tool reaches nearly 95% of agreement with manually classified instances, improving the effectiveness of related research works. Moreover, using a training dataset 15 times bigger, the time required to process each image and extract its graphical features (0.680 s) is seven times lower.This research has received funding from the CRYSTAL project – Critical System Engineering Acceleration (European Union’s Seventh Framework Program, FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement n° 332830); and from the AMASS project – Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement nº 692474; Spain’s MINECO ref. PCIN-2015-262)

Learning understandable classifier models.

The topic of this dissertation is the automation of the process of extracting understandable patterns and rules from data. An unprecedented amount of data is available to anyone with a computer connected to the Internet. The disciplines of Data Mining and Machine Learning have emerged over the last two decades to face this challenge. This has led to the development of many tools and methods. These tools often produce models that make very accurate predictions about previously unseen data. However, models built by the most accurate methods are usually hard to understand or interpret by humans. In consequence, they deliver only decisions, and are short of any explanations. Hence they do not directly lead to the acquisition of new knowledge. This dissertation contributes to bridging the gap between the accurate opaque models and those less accurate but more transparent for humans. This dissertation first defines the problem of learning from data. It surveys the state-of-the-art methods for supervised learning of both understandable and opaque models from data, as well as unsupervised methods that detect features present in the data. It describes popular methods of rule extraction from unintelligible models which rewrite them into an understandable form. Limitations of rule extraction are described. A novel definition of understandability which ties computational complexity and learning is provided to show that rule extraction is an NP-hard problem. Next, a discussion whether one can expect that even an accurate classifier has learned new knowledge. The survey ends with a presentation of two approaches to building of understandable classifiers. On the one hand, understandable models must be able to accurately describe relations in the data. On the other hand, often a description of the output of a system in terms of its input requires the introduction of intermediate concepts, called features. Therefore it is crucial to develop methods that describe the data with understandable features and are able to use those features to present the relation that describes the data. Novel contributions of this thesis follow the survey. Two families of rule extraction algorithms are considered. First, a method that can work with any opaque classifier is introduced. Artificial training patterns are generated in a mathematically sound way and used to train more accurate understandable models. Subsequently, two novel algorithms that require that the opaque model is a Neural Network are presented. They rely on access to the network\u27s weights and biases to induce rules encoded as Decision Diagrams. Finally, the topic of feature extraction is considered. The impact on imposing non-negativity constraints on the weights of a neural network is considered. It is proved that a three layer network with non-negative weights can shatter any given set of points and experiments are conducted to assess the accuracy and interpretability of such networks. Then, a novel path-following algorithm that finds robust sparse encodings of data is presented. In summary, this dissertation contributes to improved understandability of classifiers in several tangible and original ways. It introduces three distinct aspects of achieving this goal: infusion of additional patterns from the underlying pattern distribution into rule learners, the derivation of decision diagrams from neural networks, and achieving sparse coding with neural networks with non-negative weights

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

Oceanus.

v. 26, no. 2 (1983