paper:

Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS)

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code:

// best_group初始包含所有屬性 // main search loopboolean done = false;boolean addone = false;boolean z;boolean deleted = false;while (!done) {temp_group = (BitSet)best_group.clone();temp_best = best_merit;done = true;addone = false;for (i = 0; i < numAttribs;i++) {z = ((i != classIndex) && (temp_group.get(i)));if (z) {temp_group.clear(i);//TODO 核心在這。。如果數據樣本中不包含屬性i時，待測屬性集的評價分高于數據樣本中包含屬性i時待測屬性集的評價分， 則從“數據樣本”中永久性刪除該屬性（實際上是在評價時忽略該屬性）temp_merit = ((SubsetEvaluator)eval).evaluateSubset(temp_group);temp_merit_delete = ((EvalWithDelete)eval).evaluateSubsetDelete(temp_group, i);boolean deleteBetter = false;if (temp_merit_delete >= temp_merit) {temp_merit = temp_merit_delete;deleteBetter = true; //標記為刪除可能會好點， 具體刪除的更多限制條件在后面。。}z = (temp_merit >= temp_best); if (z) { //還要高于當前最佳temp_best = temp_merit;temp_index = i;addone = true;done = false;if (deleteBetter) {deleted = true;} else {deleted = false;}}// unset this addition/deletiontemp_group.set(i);}// end if(z)}//end for(i=0;if (addone) { //如果刪除該屬性后的評價分比不刪除的要高、且高于當前最高評價， 則刪除該屬性：//從best_group中永久性刪除， 從數據樣本中永久性刪除best_group.clear(temp_index);best_merit = temp_best;if (deleted) {((EvalWithDelete)eval).getDeletedList().set(temp_index);}}}// end while(!done)return attributeList(best_group);

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