Method and system for rating measured values taken from a system

2:

• The method according to claim 1, wherein step (b) further comprises:
• (b1) forming a score value set Q comprising score values q from the set V by at least one score function F:
• V→Q, vF(v)=q;
• (b2) forming a probability set P comprising probabilities p from the score value set Q by at least one transformation function T:
• Q→P, qT(q)=T(F(v))=p;
• and
• (b3) forming the modified learning set V′ from measured values, wherein the measured values v∈V are included with a respective probability of 1−p, with p=T(F(v)), into the modified learning set V′.

3:

• The method according to claim 1, wherein step (b) further comprises:
• (b1) forming a score value set Q comprising score values q from the set V by at least one score function F:
• V→Q, VF(V)=q;
• (b2) forming a probability set P comprising probabilities p from the score value set Q by at least one transformation function T:
• Q→P, qT(q)=T(F(v))=p;
• and
• (b3) forming the modified learning set V′ from measured values, wherein the measured values v∈V are included with a respective probability of 1−p, with p=T(F(v)), into the modified learning set V′;
• wherein the measured values v∈V are given a respective weighting by at least one weighting function G.

17:

• A system for rating measured values taken from a system S that may be in an error-free or erroneous state, wherein the system S comprises at least one communication network, a network component of a communication system or a service of a communication system, the system comprising a processor and a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein execution of the processor-executable instructions by the processor facilitates the following:
• forming a set Y of unmarked measured values v from the system S;
• forming a modified learning set V′ comprising measured values v′ for a learning system L by (I) removal or (h) weighting or (in) removal and weighing of measured values from the set V using a random-based method;
• forming a model M for rating measured values from the system S from the modified learning set V′;
• and
• rating measured values from the system S using the model M.

18:

• The system according to claim 17, wherein forming the modified learning set V′ further comprises:
• forming a score value set Q comprising score values q from the set V by at least one score function F:
• V→Q, vF(v)=q;
• and
• forming a probability set P with probabilities p from the score value set Q by at least one transformation function T:
• Q→P, qT(q)=T(F(v))=p;
• wherein forming the modified learning set V′ further comprises forming the modified learning set V′ of measured values by introducing the measured values v∈V with a corresponding probability of 1−p, with p−T(F(v)) into the modified learning set V′ and by weighting the measured values v∈V by at least one weighting function G;
• and
• wherein forming the modified learning set V′ further comprises removing at least one closest neighbor of the measured value v from the set V during (i) removal, or (ii) weighting or (iii) removal and weighting of measured values v.

19:

• The system according to claim 17, wherein forming the modified learning set V′ further comprises:
• forming a score value set Q comprising score values q from the set V by at least one score function F:
• V→Q, vF(v)=q;
• and
• forming a probability set P with probabilities p from the score value set Q by at least one transformation function T:
• Q→P, qT(q)=T(F(v))=p;
• and
• wherein forming the modified learning set V′ further comprises forming the modified learning set V′ of measured values by introducing the measured values v∈V with a corresponding probability of 1−p, with p=T(F(v)) into the modified learning set V′ and by weighting the measured values v∈V by at least one weighting function G.

21:

• The system according to claim 18, wherein execution of the processor-executable instructions by the processor further facilitates:
• forming the score value set Q several times;
• and
• forming the probability set P_several times;
• and
• forming the modified learning set V′ several times.