9,581 research outputs found
GOGGLES: Automatic Image Labeling with Affinity Coding
Generating large labeled training data is becoming the biggest bottleneck in
building and deploying supervised machine learning models. Recently, the data
programming paradigm has been proposed to reduce the human cost in labeling
training data. However, data programming relies on designing labeling functions
which still requires significant domain expertise. Also, it is prohibitively
difficult to write labeling functions for image datasets as it is hard to
express domain knowledge using raw features for images (pixels).
We propose affinity coding, a new domain-agnostic paradigm for automated
training data labeling. The core premise of affinity coding is that the
affinity scores of instance pairs belonging to the same class on average should
be higher than those of pairs belonging to different classes, according to some
affinity functions. We build the GOGGLES system that implements affinity coding
for labeling image datasets by designing a novel set of reusable affinity
functions for images, and propose a novel hierarchical generative model for
class inference using a small development set.
We compare GOGGLES with existing data programming systems on 5 image labeling
tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a
minimum of 71% to a maximum of 98% without requiring any extensive human
annotation. In terms of end-to-end performance, GOGGLES outperforms the
state-of-the-art data programming system Snuba by 21% and a state-of-the-art
few-shot learning technique by 5%, and is only 7% away from the fully
supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management
of Dat
Rough set methodology in meta-analysis - a comparative and exploratory analysis
We study the applicability of the pattern recognition methodology "rough set data analysis" (RSDA) in the field of meta analysis. We give a summary of the mathematical and statistical background and then proceed to an application of the theory to a meta analysis of empirical studies dealing with the deterrent effect introduced by Becker and Ehrlich. Results are compared with a previously devised meta regression analysis. We find that the RSDA can be used to discover information overlooked by other methods, to preprocess the data for further studying and to strengthen results previously found by other methods.Rough Data Set, RSDA, Meta Analysis, Data Mining, Pattern Recognition, Deterrence, Criminometrics
LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification
Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.
This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems.
We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success.
Adviser: Dr Jitender Deogu
Using XML and XSLT for flexible elicitation of mental-health risk knowledge
Current tools for assessing risks associated with mental-health problems require assessors to make high-level judgements based on clinical experience. This paper describes how new technologies can enhance qualitative research methods to identify lower-level cues underlying these judgements, which can be collected by people without a specialist mental-health background.
Methods and evolving results: Content analysis of interviews with 46 multidisciplinary mental-health experts exposed the cues and their interrelationships, which were represented by a mind map using software that stores maps as XML. All 46 mind maps were integrated into a single XML knowledge structure and analysed by a Lisp program to generate quantitative information about the numbers of experts associated with each part of it. The knowledge was refined by the experts, using software developed in Flash to record their collective views within the XML itself. These views specified how the XML should be transformed by XSLT, a technology for rendering XML, which resulted in a validated hierarchical knowledge structure associating patient cues with risks.
Conclusions: Changing knowledge elicitation requirements were accommodated by flexible transformations of XML data using XSLT, which also facilitated generation of multiple data-gathering tools suiting different assessment circumstances and levels of mental-health knowledge
Vision-based neural network classifiers and their applications
A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research.
This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL).
Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel
A Machine learning approach to POS tagging
We have applied inductive learning of statistical decision trees
and relaxation labelling to the Natural Language Processing (NLP)
task of morphosyntactic disambiguation (Part Of Speech Tagging).
The learning process is supervised and obtains a language
model oriented to resolve POS ambiguities. This model consists
of a set of statistical decision trees expressing distribution of
tags and words in some relevant contexts.
The acquired language models are complete enough to be directly
used as sets of POS disambiguation rules, and include more complex
contextual information than simple collections of n-grams usually
used in statistical taggers.
We have implemented a quite simple and fast tagger that has been
tested and evaluated on the Wall Street Journal (WSJ) corpus with
a remarkable accuracy.
However, better results can be obtained by translating the trees
into rules to feed a flexible relaxation labelling based tagger.
In this direction we describe a tagger which is able to use
information of any kind (n-grams, automatically acquired constraints,
linguistically motivated manually written constraints, etc.), and in
particular to incorporate the machine learned decision trees.
Simultaneously, we address the problem of tagging when only
small training material is available, which is crucial in any process
of constructing, from scratch, an annotated corpus. We show that quite
high accuracy can be achieved with our system in this situation.Postprint (published version
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