8,772 research outputs found
A knowledge-based approach to supervised incremental learning
[[abstract]]How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a rule-based connectionist approach in which old knowledge is preserved by bounding weight modifications. In addition, some heuristics are developed for avoiding overtraining of the network and adding new hidden units. The feasibility of this approach is demonstrated for classification problems including the iris and the promoter domains.[[conferencedate]]19940627~19940702[[conferencelocation]]Orlando, FL, US
Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classification
With the abundance of industrial datasets, imbalanced classification has
become a common problem in several application domains. Oversampling is an
effective method to solve imbalanced classification. One of the main challenges
of the existing oversampling methods is to accurately label the new synthetic
samples. Inaccurate labels of the synthetic samples would distort the
distribution of the dataset and possibly worsen the classification performance.
This paper introduces the idea of weakly supervised learning to handle the
inaccurate labeling of synthetic samples caused by traditional oversampling
methods. Graph semi-supervised SMOTE is developed to improve the credibility of
the synthetic samples' labels. In addition, we propose cost-sensitive
neighborhood components analysis for high dimensional datasets and bootstrap
based ensemble framework for highly imbalanced datasets. The proposed method
has achieved good classification performance on 8 synthetic datasets and 3
real-world datasets, especially for high imbalance and high dimensionality
problems. The average performances and robustness are better than the benchmark
methods
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