Drug activity prediction using multiple-instance learning via joint instance and feature selection
نویسندگان
چکیده
منابع مشابه
Multiple-Instance Learning: Multiple Feature Selection on Instance Representation
In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent a...
متن کاملMultiple-Instance Learning with Instance Selection via Dominant Sets
Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supe...
متن کاملRevisiting Multiple-Instance Learning Via Embedded Instance Selection
Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learner to a propositionalized version of MI data. However, the original authors consider only one single-instance base learner for the algorithm — the 1-norm SVM. We present an empirical study investigating the efficacy of...
متن کاملRobust multiple-instance learning ensembles using random subspace instance selection
Many real-world pattern recognition problems can be modeled using multiple-instance learning (MIL), where instances are grouped into bags, and each bag is assigned a label. State-of-the-art MIL methods provide a high level of performance when strong assumptions are made regarding the underlying data distributions, and the proportion of positive to negative instances in positive bags. In this pa...
متن کاملInstance Label Prediction by Dirichlet Process Multiple Instance Learning
We propose a generative Bayesian model that predicts instance labels from weak (bag-level) supervision. We solve this problem by simultaneously modeling class distributions by Gaussian mixture models and inferring the class labels of positive bag instances that satisfy the multiple instance constraints. We employ Dirichlet process priors on mixture weights to automate model selection, and effic...
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: BMC Bioinformatics
سال: 2013
ISSN: 1471-2105
DOI: 10.1186/1471-2105-14-s14-s16