This project is part of Advanced Machine learning course at Otto Von Guericke University, Magdeburg - 2019 ( http://www.dke-research.de/en/Studies/Courses/Summer+Term+2019/Advanced+Topics+in+Machine+Learning.html)
For Executing: python executor.py
RunType = 1 {For preprocessing}, 2 {For training}
Source = Folder path of training files (only for preprocessing)
Destination = Folder path for preprocessed files
ClassifierType
= 1{For OneVsRest LSVM Unigram (single label at a time)}
= 2{For OneVsRest LSVM Bigram (single label at a time)}
= 3{For OneVsRest LSVM Unigram with tfidf parameter min_df=0.01, max_df=0.8 (single label at a time)}
= 4{For OneVsRest LSVM Bigram with tfidf parameter min_df=0.01, max_df=0.8 (single label at a time)}
= 5{For OneVsRest LSVM Unigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 6{For OneVsRest LSVM bigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 7{For OneVsRest MultinomialNB Unigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 8{For OneVsRest MultinomialNB bigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 9{For OneVsRest SGDClassifier Unigram tfidf parameter min_df=0.01, max_df=0.8 }
= 10{For OneVsRest SGDClassifier Bigram tfidf parameter min_df=0.01, max_df=0.8 }
= 11{For LabelPowerset LSVM Unigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 12{For LabelPowerset LSVM bigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 13{For LabelPowerset MultinomialNB Unigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 14{For LabelPowerset MultinomialNB bigram with tfidf parameter min_df=0.01, max_df=0.8 }
= 15{For LabelPowerset SGDClassifier Unigram tfidf parameter min_df=0.01, max_df=0.8 }
= 16{For LabelPowerset SGDClassifier Bigram tfidf parameter min_df=0.01, max_df=0.8 }