Instance Selection - Data Preprocessing in Data Mining - page 198

Table 8.1 IS methods reviewed
Complete name

Abbr. name Reference

Condensed nearest neighbor CNN [ 83 ]

Reduced nearest neighbor RNN [ 76 ]

Edited nearest neighbor ENN [ 165 ]

No name specified Ullmann [ 156 ]

Selective nearest neighbor SNN [ 136 ]

Repeated edited Nearest neighbor RENN [ 149 ]

All-KNN AllKNN

Tomek condensed nearest neighbor TCNN [ 150 ]

Mutual neighborhood value MNV [ 78 ]

MultiEdit MultiEdit [ 47 , 48 ]

Shrink Shrink [ 89 ]

Instance based 2 IB2 [ 1 ]

Instance based 3 IB3

Monte carlo 1 MC1 [ 147 ]

Random mutation hill climbing RMHC

Minimal consistent set MCS [ 36 ]

Encoding length heuristic ELH [ 18 ]

Encoding length grow ELGrow

Explore Explore

Model class selection MoCS [ 16 ]

Variable similarity metric VSM [ 111 ]

Gabriel graph editing GGE [ 139 ]

Relative Neighborhood Graph Editing RNGE

Polyline functions PF [ 107 ]

Generational genetic algorithm GGA [ 100 , 101 ]

Modified edited nearest neighbor MENN [ 84 ]

Decremental reduction optimization procedure 1 DROP1 [ 167 ]

Decremental reduction optimization procedure 2 DROP2

Decremental reduction optimization procedure 3 DROP3

Decremental reduction optimization procedure 4 DROP4

Decremental reduction optimization procedure 5 DROP5

Decremental encoding length DEL

Estimation of distribution algorithm EDA [ 146 ]

Tabu search CerveronTS [ 26 ]

Iterative case filtering ICF [ 15 ]

Modified condensed nearest neighbor MCNN [ 46 ]

Intelligent genetic algorithm IGA [ 86 ]

Prototype selection using relative certainty gain PSRCG [ 143 , 144 ]

Improved KNN IKNN [ 168 ]

(continued)