[PDF] Top 20 Comparing the performance of oversampling techniques in combination with a clustering algorithm for imbalanced learning
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Comparing the performance of oversampling techniques in combination with a clustering algorithm for imbalanced learning
... datasets in supervised learning are considered an ongoing challenging task for standard algorithms, seeing as they are designed to handle balanced class distributions and perform poorly when applied to ... See full document
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A PERFORMANCE COMPARISON OF OVERSAMPLING METHODS FOR DATA GENERATION IN IMBALANCED LEARNING TASKS
... The algorithm level issue concerns the inability of algorithms to optimize learning for target evaluation criteria in the imbalanced case, which are quite different ... See full document
28
Small data oversampling: improving small data prediction accuracy using the geometric SMOTE algorithm
... In the age of Big Data, many machine learning tasks in numerous industries are still restricted due to the use of small ...datasets. The limited availability ... See full document
31
Imbalanced learning in land cover classification
... Abstract: The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource ... See full document
14
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
... problem in supervised learning as standard classification algorithms are designed to handle balanced class ...to the classification algorithm. Such techniques, called oversamplers, ... See full document
27
Techniques to deal with imbalanced data in multi-class problems: A review of existing methods
... designed with the purpose of adapting the common rebalance of classes approach used in cost-sensitive learning to tackle multi-class problems while also demonstrating good ... See full document
51
Comparing the performance of oversampling techniques for imbalanced learning in insurance fraud detection
... lot of instances belonging to the minority class are replicated, the data set could become too specific, lead to a high accuracy in the train data but to a poor classification ... See full document
25
Performance Comparison of Turbo Code in WIMAX System with Various Detection Techniques
... approach the Shannon limit by using a block code with large block length or a convolutional code with a large constraint ...length. The processing power required to decode such long codes ... See full document
5
Determinants of the management learning performance in ERP context
... through the entire organisation in real-time (Hayes and McGilsky, ...Given the in fluence of enter- prises systems on organisations performances, more and more com- panies are adopting ... See full document
10
Classification of Different Species Families using Clustering Algorithm
... follows. In section 2, two methods are used to find the threshold values and also the materials ...contains the algorithm that is used for finding the threshold values and also ... See full document
3
Spatio-temporal data mining in palaeogeographic data with a density-based clustering algorithm
... O uso da mineração de dados e do processo de descoberta de conhecimento em banco de dados (Knowledge Discovery in Databases (KDD)) vem crescendo em sua importância conforme cresce o volume de dados armazenados em ... See full document
103
Towards the Improvement of robot motion learning techniques
... Improvement with Path Integrals (PI 2 ) This is a method of probabilistic reinforce- ment learning, introduced in [Theodorou et ...from the framework of stochastic optimal ... See full document
119
Statistical tracing of magnetic fields: comparing and improving the techniques
... adopt the subblock averaging and the respective error- estimation method as suggested in ...While the gradients are good probes of magnetic field directions as suggested by our series ... See full document
16
A Multi-Criteria Decision Method in the DBSCAN Algorithm for Better Clustering
... all the limits evoked previously, this present paper proposes an approach of an unsupervised clustering algorithm based on the ...This algorithm is contributing to the ... See full document
8
A neural network clustering algorithm for the ATLAS silicon pixel detector
... estimate the compatibility of measurements with the track hypothesis under the assumptions of a track ...model. The term “measurement" is used to describe clusters ... See full document
39
The magic square as a benchmark: comparing manual solution with MIP solution and AI algorithm and improved evolutionary algorithm
... find all global solutions, Parsopoulos and Vrahatis [11 ] proposed a modification of the PSO algorithm that relies on a function stretching technique, which is used to e[r] ... See full document
9
Customer clustering in the health insurance industry by means of unsupervised machine learning
... Customer clustering can set an organisation up to reap great rewards – companies have seen their conversion rates and in turn revenue increase as well as a higher retention rate of their ...trust. ... See full document
50
CLUSTERING TWEETS USING CELLULAR GENETIC ALGORITHM
... done with clustering tweets into eight topics defined in ...advance. The formulation of the problem of clustering tweets based on their similarity is motivated by ... See full document
12
AN IMPROVED CLUSTERING ALGORITHM FOR CUSTOMER SEGMENTATION
... activities in all stages of a relationship (initiation, maintenance and termination) and business performance [Markus Wubben ...analyze the customer data and distinguish the customers ... See full document
8
Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain Images
... used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and ... See full document
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