i'm making experiments using app. 5000 labeled dataset.i'm trying different supervised ML algorithm to evaluate the results.The vector size is 13 with the labels (totally 12 features+1 label) and i have 15 vector of labeled "flower" class. experiments consist of all data set using 10k cross validation. All features are continuous.
1 experiments using the "pure" features of all dataset.
2 experiments using only "one" feature (out of 12) change of the flower class.
i applied naive bayes, C4.5 but all results of 1 and 2 is same, however logistic regression gives different results and lasted longer.
1- To your best experiences, what causes the difference between naive bayes, c.45 and logistic regression, how should i evaluate the results to make the audiences satisfied?
2- if performance is an important metric, and classifier is used for IDS systems, which ML algorithms do you offer?
Edit: More explanation to make the question clear:
we have 8 different class labels. flower + other 7 labels. In experiment 2 we change the only one attribute of the flower class out of 12 attributes (15 flower labeled class stays same but only one attribute is changed.). all other dataset stays same. so we make experiments using logistic regression, naive bayes and c4.5 seperately with two different dataset. (1- with 5000 dataset, 2- other dataset has difference of change in one attribute of flower class, all other classes stays same).
comparision: we have results of situation number 1 and 2, in C4.5 and naive bayes, nothing changes, FP and FN. but logistic regression gives interesting results.
12 0.4 0.4 0.5 2.333 434 12.2 10 2 10 12 12 flower
........................................... flower
...........................................
........................................... flower (total 15 flower class.)
// one feature change 2nd feature.
12 0.8 0.4 0.5 2.333 434 12.2 10 2 10 12 12 flower
........................................... flower
...........................................
........................................... flower (total 15 flower class,2nd feature all changed.)
For example can i make i comment like that: because C4.5 uses the maximum number of class in leaf nodes, the change of one feature in flower class will not affect/change the leaf node classes However, logistic regression uses ... so we observe this kind of differences.???