The decision tree algorithm for monotone classification presented in [4, 10] requires strictly monotone data sets. This paper addresses the problem of noise due to violation of the monotonicity constraints and proposes a modification of the algorithm to handle noisy data. It also presents...

We consider generalized monotone functions f: X -- {0,1} defined for an arbitrary binary relation = on X by the property x = y implies f(x) = f(y). These include the standard monotone (or positive) Boolean functions, regular Boolean functions and other interesting functions as special cases. It...

We consider generalized monotone functions f: X -- {0,1} defined for an arbitrary binary relation = on X by the property x = y implies f(x) = f(y). These include the standard monotone (or positive) Boolean functions, regular Boolean functions and other interesting functions as special cases. It...

The decision tree algorithm for monotone classification presented in [4, 10] requires strictly monotone data sets. This paper addresses the problem of noise due to violation of the monotonicity constraints and proposes a modification of the algorithm to handle noisy data. It also presents...

The bankruptcy prediction problem can be considered an or dinal classification problem. The classical theory of Rough Sets describes objects by discrete attributes, and does not take into account the order- ing of the attributes values. This paper proposes a modification of the Rough Set...

The bankruptcy prediction problem can be considered an or dinal classification problem. The classical theory of Rough Sets describes objects by discrete attributes, and does not take into account the order- ing of the attributes values. This paper proposes a modification of the Rough Set...