
| ahere                                                      | -adhere   |      |    |
|------------------------------------------------------------|-----------|------|----|
| ( No ongoing project ) ∧ ( No education )                  | -1        |      |    |
| -                                                          |           |      |    |
| ( No ongoing project ) ∧ ( engthy proc) ∧ ( No W holesaler | -1        |      |    |
| default  D                                                 | 0.32      | 0.68 | 0  |

Figure 3: The DC obtained on the agricultural data (see text for the interpretation of the variables).

We ran both algorithms in a 10-fold stratified cross-validation experiment. WIDC(p)
obtained a 2.8% average error. In 6 out of 10 runs, the same DC was induced. It is presented in Figure 3. Basically, this DC proves that predicting the "—adhere" class is the easiest task, followed by the prediction of the "adhere" class. The "?" (uncertain farmers) is predicted only by the default vector. This seems rather natural: whereas the extreme behaviors tend to be clear to determine, the uncertainty is the hardest to predict.

C4.5 (default parameters) induced a DT which was almost the exact transcription of rule 1, a rule which says that farmers with no education (without any agricultural diploma or traineeships) and no ongoing project are not willing to adhere.  This rule is mostly interesting because it proves that education is a strong factor determining the "—adhere" answer. The DTs induced also contained one or two more literals separating the "adhere" and "?" classes (average error: 6.7%), but only few other things could be mined from the trees of C4.5, in the light of the problem addressed.

Rule 2 in Figure 3 did not have the equivalent in the DTs induced.  What it says is interesting for the DDAF, because it brings the following conclusion: farmers without ongoing projects, and not selling their products only to a wholesaler, are on the knife edge for their membership (either in "adhere", or in "¬adhere").  Without going further into local agricultural considerations, this rule, for the DDAF Engineers, represents an accurate view of the farmers actually controlling their exploitation costs, being either for or against CTEs, and that education pushes towards the membership (combination of rules 1 and 2),
probably because it allows them to see the future potential benefits of the contract, better than its current constraints.

## 6.3 Noise Handling

Noise handling is a crucial issue for boosting (Bauer & Kohavi, 1999; Opitz & Maclin, 199), even considered (Bauer & Kohavi, 1999) as its potential main problem. Experimental studies show that substantial noise levels can alter the vote to the point that its accuracy is lower than that of a single of its classifier (Opitz & Maclin, 1999).  Opitz & Maclin (1999) point out the reweighting scheme of the examples in boosting as being a potential reason for this behavior. Though we do not use any reweighting scheme, we have chosen for the sake of completeness to address the behavior of WIDC(p) against noise, and compare its results with perhaps the major induction algorithm with which we share the "topdown and prune" induction scheme: C4.5 (Quinlan, 1994). This study relies on the XD6 domain, in whi we replace the original 10% class noise (Buntine & Niblett, 1992) by various increasing amounts of class noise ranging from 0% to 40% by steps of 2%, or various