2,473 research outputs found
Towards the Application of Association Rules for Defeasible Rules Discovery
In this paper we investigate the feasibility of Knowledge Discovery from Database (KDD) in order to facilitate the discovery of defeasible rules that represent the ratio decidendi underpinning legal decision making. Moreover we will argue in favour of Defeasible Logic as the appropriate formal system in which the extracted principles should be encoded
Prescriptive formalism for constructing domain-specific evolutionary algorithms
It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m..
Learning from interpreting transitions in explainable deep learning for biometrics
Máster Universitario en Métodos Formales en
IngenierÃa InformáticaWith the rapid development of machine learning algorithms, it has been
applied to almost every aspect of tasks, such as natural language processing, marketing
prediction. The usage of machine learning algorithms is also growing in human resources
departments like the hiring pipeline. However, typical machine learning algorithms learn
from the data collected from society, and therefore the model learned may inherently reflect
the current and historical biases, and there are relevant machine learning algorithms that
have been shown to make decisions largely influenced by gender or ethnicity. How to
reason about the bias of decisions made by machine learning algorithms has attracted more
and more attention. Neural structures, such as deep learning ones (the most successful
machine learning based on statistical learning) lack the ability of explaining their decisions.
The domain depicted in this point is just one example in which explanations are needed.
Situations like this are in the origin of explainable AI. It is the domain of interest for this
project. The nature of explanations is rather declarative instead of numerical. The
hypothesis of this project is that declarative approaches to machine learning could be
crucial in explainable A
A critical examination and development of Wellman’s theory of conductive argument
The paper aims to provide an analysis and critique of Carl Wellman’s account of conduction presented in Challenge and Response and Morals and Ethics. It considers several issues, including: reason-ing vs. argument, the definition vs. the three patterns of conduction, pro and con arguments as dialogues, their assessment, the concept of validity, applications beyond moral arguments, argument type vs. as crite-rion of evaluation
Methodology of Algorithm Engineering
Research on algorithms has drastically increased in recent years. Various
sub-disciplines of computer science investigate algorithms according to
different objectives and standards. This plurality of the field has led to
various methodological advances that have not yet been transferred to
neighboring sub-disciplines. The central roadblock for a better knowledge
exchange is the lack of a common methodological framework integrating the
perspectives of these sub-disciplines. It is the objective of this paper to
develop a research framework for algorithm engineering. Our framework builds on
three areas discussed in the philosophy of science: ontology, epistemology and
methodology. In essence, ontology describes algorithm engineering as being
concerned with algorithmic problems, algorithmic tasks, algorithm designs and
algorithm implementations. Epistemology describes the body of knowledge of
algorithm engineering as a collection of prescriptive and descriptive
knowledge, residing in World 3 of Popper's Three Worlds model. Methodology
refers to the steps how we can systematically enhance our knowledge of specific
algorithms. The framework helps us to identify and discuss various validity
concerns relevant to any algorithm engineering contribution. In this way, our
framework has important implications for researching algorithms in various
areas of computer science
Data mining approaches for detecting intrusion using UNIX process execution traces
Intrusion detection systems help computer systems prepare for and deal with malicious attacks. They collect information from a variety of systems and network sources, then analyze the information for signs of intrusion and misuse. A variety of techniques have been employed to analyze the information from traditional statistical methods to new emerged data mining approaches. In this thesis, we describe several algorithms designed for this task, including neural networks, rule induction with C4.5, and Rough sets methods. We compare the classification accuracy of the various methods in a set of UNIX process execution traces. We used two kinds of evaluation methods. The first evaluation criterion characterizes performances over a set of individual classifications in terms of average testing accuracy rate. The second measures the true and false positive rates of the classification output over certain threshold. Experiments were run on data sets of system calls created by synthetic sendmail programs. There were two types of representation methods used. Different combinations of parameters were tested during the experiment. Results indicate that for a wide range of conditions, Rough sets have higher classification accuracy than that of Neural networks and C4.5. In terms of true and false positive evaluations, Rough sets and Neural networks turned out to be better than C4.5
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