By Max Bramer, Miltos Petridis
The papers during this quantity are the refereed papers awarded at AI-2014, the Thirty-fourth SGAI overseas convention on leading edge options and purposes of man-made Intelligence, held in Cambridge in December 2014 in either the technical and the appliance streams.
They current new and cutting edge advancements and functions, divided into technical circulation sections on wisdom Discovery and knowledge Mining, computer studying, and brokers, Ontologies and Genetic Programming, through software flow sections on Evolutionary Algorithms/Dynamic Modelling, making plans and Optimisation, and desktop studying and information Mining. the amount additionally comprises the textual content of brief papers awarded as posters on the conference.
This is the thirty-first quantity within the Research and improvement in clever Systems sequence, which additionally accommodates the twenty-second quantity within the Applications and recommendations in clever Systems sequence. those sequence are crucial interpreting in case you desire to sustain so far with advancements during this vital field.
Read or Download Research and Development in Intelligent Systems XXXI: Incorporating Applications and Innovations in Intelligent Systems XXII PDF
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B 95 % of all possible values Fig. 5 Distributions of different classification overlap each other However, we are interested to find a rule term, which can maximise the coverage of the rule for a target class. Therefore, our approach uses density estimation to discover a rule term in the form of (x < α ≤ y) by selecting only a highly relevant range of values from a continuous attribute, which can then be used to represent a subset of instances for the target class along with other rule terms. Our approach for rule induction from continuous attributes can be described with the following steps: 1.
5. Fig. 3 Shaded area represents a range of values of attribute α for class ωi Computationally Efficient Rule-Based Classification for Continuous Streaming Data 27 Fig. 4 Shaded area represents a range of values of attribute α for class ωi . a 68 % of all possible values. b 95 % of all possible values Fig. 5 Distributions of different classification overlap each other However, we are interested to find a rule term, which can maximise the coverage of the rule for a target class. Therefore, our approach uses density estimation to discover a rule term in the form of (x < α ≤ y) by selecting only a highly relevant range of values from a continuous attribute, which can then be used to represent a subset of instances for the target class along with other rule terms.
Fig. 3 Shaded area represents a range of values of attribute α for class ωi Computationally Efficient Rule-Based Classification for Continuous Streaming Data 27 Fig. 4 Shaded area represents a range of values of attribute α for class ωi . a 68 % of all possible values. b 95 % of all possible values Fig. 5 Distributions of different classification overlap each other However, we are interested to find a rule term, which can maximise the coverage of the rule for a target class. Therefore, our approach uses density estimation to discover a rule term in the form of (x < α ≤ y) by selecting only a highly relevant range of values from a continuous attribute, which can then be used to represent a subset of instances for the target class along with other rule terms.