Add to ORKGCluster test selection is proposed as an efficient regression testing approach. It uses some distance measures and clustering algorithms to group tests into some clusters. Tests in a same cluster are considered to have similar behaviors. A certain sampling strategy for the clustering result is used to build up a small subset of tests, which is expected to approximate the fault detection capability of the original test set. All existing cluster test selection methods employ unsupervised clustering. The previous test results are not used in the process of clustering. It may lead to unsatisfactory clustering results in some cases. In this paper, a semi-supervised clustering method, namely semi-supervised K-means (SSKM), is introduced to improv...
National audienceThe success of machine learning approaches to solving real-world problems motivated...
Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Prev...
Pair-wise comparison has been successfully utilised in order to prioritise test cases by exploiting ...
Abstract—In order to improve the efficiency of regression testing, many test selection techniques ha...
Although there is a large and growing literature that tackles the semi-supervised clustering problem...
Although there is a large and growing literature that tackles the semi-supervised clustering problem...
One of the key tools to gain knowledge from data is clustering: identifying groups of instances that...
Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering p...
Cluster test selection is a new successful approach to select a subset of the existing test suite in...
Abstract — Semisupervised classification (SSC) learns, from cheap unlabeled data and labeled data, t...
In many machine learning domains (e.g. text processing, bioinformatics), there is a large supply of ...
Data mining is the process of finding the previously unknown and potentially interesting patterns an...
In this dissertation, a Semiparametric density ratio testing method which bor-rows strength from two...
Clustering methods are developed for categorizing data points into different groups so that data poi...
Abstract Background A semiparametric density ratio method which borrows strength from two or more sa...
National audienceThe success of machine learning approaches to solving real-world problems motivated...
Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Prev...
Pair-wise comparison has been successfully utilised in order to prioritise test cases by exploiting ...
Abstract—In order to improve the efficiency of regression testing, many test selection techniques ha...
Although there is a large and growing literature that tackles the semi-supervised clustering problem...
Although there is a large and growing literature that tackles the semi-supervised clustering problem...
One of the key tools to gain knowledge from data is clustering: identifying groups of instances that...
Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering p...
Cluster test selection is a new successful approach to select a subset of the existing test suite in...
Abstract — Semisupervised classification (SSC) learns, from cheap unlabeled data and labeled data, t...
In many machine learning domains (e.g. text processing, bioinformatics), there is a large supply of ...
Data mining is the process of finding the previously unknown and potentially interesting patterns an...
In this dissertation, a Semiparametric density ratio testing method which bor-rows strength from two...
Clustering methods are developed for categorizing data points into different groups so that data poi...
Abstract Background A semiparametric density ratio method which borrows strength from two or more sa...
National audienceThe success of machine learning approaches to solving real-world problems motivated...
Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Prev...
Pair-wise comparison has been successfully utilised in order to prioritise test cases by exploiting ...