By Max Bramer, Miltos Petridis
The papers during this quantity are the refereed papers offered at AI-2015, the Thirty-fifth SGAI foreign convention on cutting edge options and functions of man-made Intelligence, held in Cambridge in December 2015 in either the technical and the appliance streams.
They current new and cutting edge advancements and functions, divided into technical movement sections on wisdom Discovery and information Mining, desktop studying and information Acquisition, and AI in motion, by means of program circulation sections on purposes of Genetic Algorithms, purposes of clever brokers and Evolutionary strategies, and AI functions. the quantity additionally comprises the textual content of brief papers provided as posters on the conference.
This is the thirty-second quantity within the Research and improvement in clever Systems sequence, which additionally contains the twenty-third quantity within the Applications and techniques in clever Systems sequence. those sequence are crucial studying if you happen to desire to sustain to this point with advancements during this vital field.
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Additional resources for Research and Development in Intelligent Systems XXXII: Incorporating Applications and Innovations in Intelligent Systems XXIII
Example text
An ensemble model is a composite model comprised of a number of learners (classifiers), called base learners or weak learners, that are used together to obtain a better classification performance than can be obtained when using a single “stand alone” model. If the base learners in an ensemble model are all comprised of the same classification algorithm the ensemble model is referred to as an homogeneous learner, while when different classification algorithms are used the ensemble model is referred to as heterogeneous learner [17].
The proportion of each type in the next period is given by the replicator equation as a function of the type’s payoffs and its current proportion in the population. Types that score above the average payoffs increase in proportion, while types that score below the average payoffs decrease in proportion. The amount of increase or decrease depends on a type’s proportion in the current population and on it’s relative payoffs. The most general continuous form is given by the differential equation ẋ i = xi [fi (x) − ????(x)] such that ????(x) = n ∑ xj fj (x) (6) (7) j=1 where xi is the proportion of type i in the population, x = (x1 , … , xn ) is the vector of the distribution of types in the population, fi (x) is the fitness of type i (which is dependent on the population), and ????(x) is the average population fitness (given by the weighted average of the fitness of the n types in the population).
All the DRFs created had an initial size of 500 trees. We used 2 subspace factors of 2 and 4 %. According to Eq. 1, these factors produced DRFs with 10 and 20 sub-forests respectively. We used a random 70 % of the features for each subspace, which has proved empirically to lead good performance in the traditional version of DRF. By Eq. 2, each sub-forest contained 50 trees for the DRF with 10 sub-forests, and 25 trees for the DRF with 20 sub-forests. For the number of iterations (refer to Number of Iterations in Algorithm 1 above), we used 25, 50, 100, 150, and 1000 iterations.