A Novel Learning-Based PLST Algorithm for Multi-Label Classification

AbstractIn Multi-Label Classification (MLC), each data sample is characterized by multiple labels. In ML, there is no restriction on the number of classes a data sample could belong to. Various methods are proposed to construct a model to map data samples to the class labels. Transforming the MLC problem into several binary classification problems is the baseline approach, also called the binary relevance technique. As an alternative solution, the label powerset approach transforms the MLC problem into a multi-class classification problem by building a binary classifier for every label composition. However, in Ensemble Methods (EMs), multiple classifiers are consolidated to form a multi-label ensemble classifier. In this case, each classifier predicts a class label. Thereupon, the predicted class labels are joined using an ensemble method. This paper presents a neoteric EM classifier using the Ridge Regression-based Principal Label Space Transformation (RR-PLST). In the proposed model, the PLST utilizes SVD to discover correlations between the labels and data samples. Dimensionality reduction is a well-known technique to enhance the model's precision and diminish execution time. Consequently, the OPLS technique is recruited to reduce data dimensions in the proposed model. Moreover, in the proposed model, a Q-Learning method is used to amend the initially predicted labels. To demonstrate the effectiveness of the proposed model, vast experiments are conducted on the various widely used multi-label datasets. The proposed algorithm results are compared with similar algorithms by the AP, EM, HS, Macro, and Micro F1 metrics. The experimental results demonstrate the superiority of the proposed model.KEYWORDS: Dimensionality reductionhybrid MLC methodnormalizationQ-learningridge regression Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSeyed Hossein Seyed EbrahimiSeyed Hossein Seyed Ebrahimi is a PhD student of computer software engineering at Islamic Azad University of Urmia, Urmia, Iran. Moreover, he received his MSc degree in computer software engineering from Islamic Azad University of Qazvin, Qazvin, Iran, in 2015, and his BSc degree in computer engineering from University of Urmia, Urmia, Iran, in 2011. Currently he is working as computer science teacher in Sardasht. His current research areas include the machine learning, reinforcement learning, and big data. Email: h.seyedebrahimi@iaurmia.ac.irKambiz MajidzadehKambiz Majidzadeh was born in Urmia, Iran, in 1980. He received the BSc degree in software engineering from the Islamic Azad University of Khoy, Khoy, Iran, in 2002, and the MSc degree in computer networking and the PhD degree in information technology from Baku State University (BSU), Baku, Azerbayijan in 2005 and 2009, respectively. His research interests are very large-scale integration design and computer networking. Corresponding author. Email: Kambiz.Majidzadeh@iau.ac.irFarhad Soleimanian GharehchopoghFarhad Soleimanian Gharehchopogh was born in 1979 in Iran. He received his BS in computer engineering from Shabestar Branch, Islamic Azad University, West Azerbaijan/ Iran, in 2002, the MS in computer engineering from Cukurova University, Adan/ Turkey, in 2011 and the PhD degree in computer engineering from Hacettepe University, Ankara/Turkey in 2015. He is an assistant professor in department of computer engineering at Urmia Branch, Islamic Azad University, Urmia, IRAN, form 2015 to know. Email: bonab.farhad@gmail.com