CAJ | 학술논문

半监督文本分类中已标记数据与未标记数据分布不一致，可能导致分类器性能较低。为此，提出一种利用蚁群聚集信息素浓度的半监督文本分类算法。将聚集信息素与传统的文本相似度计算相融合，利用Top-k策略选取出未标记蚂蚁可能归属的种群，依据判断规则判定未标记蚂蚁的置信度，采用随机选择策略，把置信度高的未标记蚂蚁加入到对其最有吸引力的训练种群中。在标准数据集上与朴素贝叶斯算法和EM算法进行对比实验，结果表明，该算法在精确率、召回率以及F1度量方面都取得了更好的效果。
반감독문본분류중이표기수거여미표기수거분포불일치，가능도치분류기성능교저。위차，제출일충이용의군취집신식소농도적반감독문본분류산법。장취집신식소여전통적문본상사도계산상융합，이용Top-k책략선취출미표기마의가능귀속적충군，의거판단규칙판정미표기마의적치신도，채용수궤선택책략，파치신도고적미표기마의가입도대기최유흡인력적훈련충군중。재표준수거집상여박소패협사산법화EM산법진행대비실험，결과표명，해산법재정학솔、소회솔이급F1도량방면도취득료경호적효과。
There are many algorithms based on data distribution to effectively solve semi-supervised text categorization. However,they may perform badly when the labeled data distribution is different from the unlabeled data. This paper presents a semi-supervised text classification algorithm based on aggregation pheromone, which is used for species aggregation in real ants and other insects. The proposed method,which has no assumption regarding the data distribution, can be applied to any kind of data distribution. In light of aggregation pheromone,colonies that unlabeled ants may belong to are selected with a Top-k strategy. Then the confidence of unlabeled ants is determined by a judgment rule. Unlabeled ants with higher confidence are added into the most attractive training colony by a random selection strategy. Compared with Na?ve Bayes and EM algorithm,the experiments on benchmark dataset show that this algorithm performs better on precision,recall and Macro F1.