4,909 research outputs found
Self-growing neural network architecture using crisp and fuzzy entropy
The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
A survey of cost-sensitive decision tree induction algorithms
The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field
Review of Nature-Inspired Forecast Combination Techniques
Effective and efficient planning in various areas can be significantly supported by forecasting a variable
like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same
variable is often available, leading to the question whether one should choose one of the single models or combine
several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast
combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are
nonlinear and have in some way been inspired by nature
Data mining in manufacturing: a review based on the kind of knowledge
In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques
Decision tree learning for intelligent mobile robot navigation
The replication of human intelligence, learning and reasoning by means of computer
algorithms is termed Artificial Intelligence (Al) and the interaction of such
algorithms with the physical world can be achieved using robotics. The work described in
this thesis investigates the applications of concept learning (an approach which takes its
inspiration from biological motivations and from survival instincts in particular) to robot
control and path planning. The methodology of concept learning has been applied using
learning decision trees (DTs) which induce domain knowledge from a finite set of training
vectors which in turn describe systematically a physical entity and are used to train a robot
to learn new concepts and to adapt its behaviour.
To achieve behaviour learning, this work introduces the novel approach of hierarchical
learning and knowledge decomposition to the frame of the reactive robot architecture.
Following the analogy with survival instincts, the robot is first taught how to survive in
very simple and homogeneous environments, namely a world without any disturbances or
any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex
environments by adding further worlds to its existing knowledge. The repertoire of the
robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered
decision trees (DTs) accommodating a number of primitives. To classify robot perceptions,
control rules are synthesised using symbolic knowledge derived from searching the
hierarchy of DTs.
A second novel concept is introduced, namely that of multi-dimensional fuzzy associative
memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained
locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to
deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs.
In this thesis, the feasibility of the developed techniques is illustrated in the robot
applications, their benefits and drawbacks are discussed
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