Mutual-Information-SMOTE: A Cost-Free Learning Method for Imbalanced Data Classification

Chen, Ying; Chen, Yufei; Liu, Xianhui; Zhang, Weidong

doi:10.1007/978-981-13-1648-7_2

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Another work on text classification also introduced a successful MI-constrained oversampling mechanism (MISO) that safely and robustly re-embeds challenging samples [135]. The MI-based SMOTE is also widely applied to multiple aspects with various classifiers, including the MI classifier [136], the KNN classifier, and the decision tree classifier [137]. Several studies also show that MI performs well for FTS problems.…”

Section: Information Theorymentioning

confidence: 99%

Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness

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It is a meaningful and unforgettable journey toward my Doctoral degree and a critical time that helps me not only Enhance my research abilities but also gain a fresh perspective on life and rediscover myself."A thousand people, a thousand Hamlets." I believe it works for how we feel about this journey. For me, it is more like a war with myself, in which I would like to overcome resistance to change, be brave, and move on. There was a time when I mistook stability for safety and comfort, but after a while, I realized that stability comes from changes since the essence of life is change. Hence, I sincerely appreciate those who support me and encourage me to be brave and complete this vital journey. Without them, I cannot make any of these advancements.I want to first express my most heartfelt appreciation to Professor Nasser Fard, my advisor, for his unwavering support, guidance, and empathy during my graduate studies. He is always there to offer me support whenever I come up with new research ideas. my research would not have been possible to start, continue, or finish without his support. My foremost gratitude also goes to my committees, Prof. Chun-An Chou, Prof. Wei Xie, and Prof. Keivan Sadeghzadeh. I thank them for their wholehearted endorsement, including helpful suggestions that contributed greatly to this dissertation and valuable insights into my research.I also would like to thank my fellow Ph.D. colleagues in the MIE department for their support and encouragement throughout my graduate studies. I cherish all my memories with them, from the midnight cramming and refreshment breaks to uplifting affirmations and talks.Finally, I would like to thank my family, especially my parents. Their caring nature towards me never ceased despite the distance, and I still remember the warmth I felt every time I received an overseas call from them. They have always been my harbor and have made me brave enough to face all challenges I met in this journey. It is their unconditional backing that has formed the cornerstone of my life.

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Section: Information Theorymentioning

confidence: 99%

Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness

View full text Add to dashboard Cite

show abstract

Mutual-Information-SMOTE: A Cost-Free Learning Method for Imbalanced Data Classification

Cited by 1 publication

References 18 publications

Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness

Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness

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