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more reasonable results with respect to random guess-
ing. Moreover, none of the 15 ensembles was ranked
in the first position across different datasets. The E0F
ensembles were more reasonable than the other en-
sembles in four datasets; the ENF ensembles were
the best in two datasets (COCOMO81 and Miyazaki).
However, the E1F ensembles were the less reasonable
in all datasets.
However, the accuracy results in terms of 8 perfor-
mance measures suggest that the ENF, in particular
with the combiners IR or ME, outperformed the E1F
and E0F ensembles in 5 out of 6 datasets. This im-
plies that using different feature subsets by ensemble
members can lead to more accurate estimations than
when members use the same feature subset or all the
available features. In fact, the success of the ENF en-
sembles is mainly due to the fact that their members
were diverse and generate different estimations at the
same point (i.e. diversity) than the members of E1F or
E0F. Moreover, E0F ensembles generate slightly bet-
ter estimates than E1F. Therefore, we conclude that
ensembles without feature selection were better and
easier to construct than ensembles with one filter.
Ongoing work will focus on investigating the im-
pact of other feature selection techniques, including
filters or wrappers, on the accuracy of homogenous
and heterogeneous ensembles.
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