Demonstration – 4ft-Miner

# Selection of the attributes

This section continues in explanation of the creating new task part described in previous section. Here will be shown how to:

## Entering cedents

By cedent are meant antecedent, succedent and condition. These types are entered the same way, so they will be described together as cedents. Each cedent consists of one or more partial cedents. There is already created one partial cedent called antecedent or succedent or condition. It only depends on user if another partial cedent will be used. Sometimes it is useful to use more partial cedents especially if it is desirable to influence more the final hypothesis. Each partial cedent has as a parametr minimal and maximal length. This length determine the minimum and maximum number of attributes (for each partial cedent) that will be in final hypotheses.

For example: if we have these three attributes (age, salary, district) in one partial cedent and the minimum length is set 1 and maximum length is set 2 than there will be only these combinations of attributes in final hypotheses:

• age,
• salary,
• district,
• age salary,
• age district,
• salary district.

## Selecting attributes

When partial cedents are defined it is possible to fill them up with attributes. For each attribute it is possible to set some parameters as can be seen from fig. 1.

Literal type is the first parameter and it has just two possibilities. Basic or Remaining. This parametr has reason when there are more then one attribut in partial cedent and can be combinated with length of the partial cedent for better results. Purpose of this parameter is, that at least one attribute set as basic will be in final hypothesis. Not every attribute set as basic but at least one.

Another parameter is Gace type. Gace type has three possibilities as can be seen from fig. 1 (positive, negative, both). Both means positive and negative. If positive gace type is selected then the attribute will be in final hypothesis in its positive form. If negative is selected then will be negation of this attribute in the final hypothesis.

The last parameter is Coefficient type. Coefficient types will be described as an example, so we suppose that these coefficients of attribute are given: 1, 2, 3 and the length of coefficient is set: minimum length = 1 and maximum length = 2:

subsets
in this example the subsets are {1}, {2}, {3}, {1, 2}, {1, 3}, {2, 3},
one category
here the maximum and minimum length is 1 so it is possible to choose only one of these coefficients {1}, {2}, {3},
interval
is similar to subsets but when interval is used the coefficients must be one next to another. Intervals are in this example {1}, {2}, {3}, {1, 2}, {2, 3},
cyclic intervals
are similar to interval but here is possible to do the cycle from last coefficient to first. Cyclic intervals are {1}, {2}, {3}, {1, 2}, {2, 3}, {3, 1},
left cut
means the minimum and maximum length from the begining {1}, {1, 2},
right cut
means the minimum and maximum length from the end {3}, {3, 2},
cut
means both left and right cuts {1}, {1, 2}, {3}, {3, 2},
boolean true
can be applied for boolean attributes and in this case only true values are considered,
boolean false
can be applied for boolean attributes and in this case only false values are considered,
both boolean
can be applied for boolean attributes and in this case both (true, false) values are considered.

## Classes of equivalence

Classes of equivalence is useful tool when it is not desirable to have some group of attributes together in one hypothesis. Classes of equivalence will ensure, that there will be displayed only hypotheses where there will be only one attribut from the class of equivalence in the cedent.

Figure 1: Attribute's parameters