Manifold embedded distribution adaptation for cross‐project defect prediction
نویسندگان
چکیده
منابع مشابه
Modeling of Real Defect Outlines for Defect Size Distribution and Yield Prediction
For efficient yield prediction defects are usually modeled by circular discs or squares. This paper presents a more accurate model that considers the real outline of physical defects. To utilize this model only the maximum and the minimum extension of detected defects have to be determined. That can be done easily using a checkerboard test structure including a defect localization procedure.
متن کاملManifold-based Similarity Adaptation for Label Propagation
Label propagation is one of the state-of-the-art methods for semi-supervised learning, which estimates labels by propagating label information through a graph. Label propagation assumes that data points (nodes) connected in a graph should have similar labels. Consequently, the label estimation heavily depends on edge weights in a graph which represent similarity of each node pair. We propose a ...
متن کاملKernel Manifold Alignment for Domain Adaptation
The wealth of sensory data coming from different modalities has opened numerous opportunities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. However, multimodal architectures must rely on models able to adapt to changes in the data distribution. Differences in the density fu...
متن کاملSubgroup Discovery for Defect Prediction
Although there is extensive literature in software defect prediction techniques, machine learning approaches have yet to be fully explored and in particular, Subgroup Discovery (SD) techniques. SD algorithms aim to find subgroups of data that are statistically different given a property of interest [1,2]. SD lies between predictive (finding rules given historical data and a property of interest...
متن کاملSupervised Manifold Learning for Media Interestingness Prediction
In this paper, we describe the models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer. Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels. Meanwh...
متن کاملذخیره 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: IET Software
سال: 2020
ISSN: 1751-8806,1751-8814
DOI: 10.1049/iet-sen.2019.0389