Consensus Clustering-Based Undersampling Approach to Imbalanced Learning

نویسندگان

چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

متن کامل

ClusterOSS: a new undersampling method for imbalanced learning

A dataset is said to be imbalanced when its classes are disproportionately represented in terms of the number of instances they contain. This problem is common in applications such as medical diagnosis of rare diseases, detection of fraudulent calls, signature recognition. In this paper we propose an alternative method for imbalanced learning, which balances the dataset using an undersampling s...

متن کامل

Learning from Imbalanced Data Using Ensemble Methods and Cluster-Based Undersampling

Imbalanced data, where the number of instances of one class is much higher than the others, are frequent in many domains such as fraud detection, telecommunications management, oil spill detection and text classification. Traditional classifiers do not perform well when considering data that are susceptible to both within-class and between-class imbalances. In this paper, we propose the ClustFi...

متن کامل

Consensus clustering by graph based approach

In this paper, we propose G-Cons, an extension of a graph minimal coloring paradigm for consensus clustering. Based on the coassociation values between data, our approach is a graph partitioning one which yields a combined partition by maximizing an objective function given by the average mutual information between the consensus partition and all initial combined clusterings. It exhibits more i...

متن کامل

A Novel Approach for Handling Imbalanced Data in Medical Diagnosis using Undersampling Technique

In many data mining applications the imbalanced learning problem is becoming ubiquitous nowadays. When the data sets have an unequal distribution of samples among classes, then these data sets are known as imbalanced data sets. When such highly imbalanced data sets are given to any classifier, then classifier may misclassify the rare samples from the minority class. To deal with such type of im...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Scientific Programming

سال: 2019

ISSN: 1058-9244,1875-919X

DOI: 10.1155/2019/5901087