Maturski, Seminarski , Maturalni i diplomski radovi iz ekonomije: menadzment, marketing, finansija, elektronskog poslovanja, internet tehnologija, biznis planovi, makroekonomija, mikroekonomija, preduzetnistvo, upravljanje ljudskim resursima, carine i porezi.


Abstract. Rule learning is typically used in solving classification and
prediction tasks. However, learning of classification rules can be adapted
also to subgroup discovery. This paper shows how this can be achieved by
modifying the CN2 rule learning algorithm. Modifications include a new
covering algorithm (weighted covering algorithm), a new search heuristic
(weighted relative accuracy), probabilistic classification of instances, and
a new measure for evaluating the results of subgroup discovery (area
under ROC curve). The main advantage of the proposed approach is that
each rule with high weighted accuracy represents a ‘chunk’ of knowledge
about the problem, due to the appropriate tradeoff between accuracy
and coverage, achieved through the use of the weighted relative accuracy
heuristic. Moreover, unlike the classical covering algorithm, in which only
the first few induced rules may be of interest as subgroup descriptors with
sufficient coverage (since subsequently induced rules are induced from
biased example subsets), the subsequent rules induced by the weighted
covering algorithm allow for discovering interesting subgroup properties
of the entire population. Experimental results on 17 UCI datasets are
very promising, demonstrating big improvements in number of induced
rules, rule coverage and rule significance, as well as smaller improvements
in rule accuracy and area under ROC curve...