222 research outputs found
GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
Neural network models often struggle with high-dimensional but small
sample-size tabular datasets. One reason is that current weight initialisation
methods assume independence between weights, which can be problematic when
there are insufficient samples to estimate the model's parameters accurately.
In such small data scenarios, leveraging additional structures can improve the
model's training stability and performance. To address this, we propose
GCondNet, a general approach to enhance neural networks by leveraging implicit
structures present in tabular data. We create a graph between samples for each
data dimension, and utilise Graph Neural Networks (GNNs) for extracting this
implicit structure, and for conditioning the parameters of the first layer of
an underlying predictor MLP network. By creating many small graphs, GCondNet
exploits the data's high-dimensionality, and thus improves the performance of
an underlying predictor network. We demonstrate the effectiveness of our method
on nine real-world datasets, where GCondNet outperforms 14 standard and
state-of-the-art methods. The results show that GCondNet is robust and can be
applied to any small sample-size and high-dimensional tabular learning task.Comment: Early version presented at the 17th Machine Learning in Computational
Biology (MLCB) meeting, 202
Exploiting physico-chemical properties in string kernels
<p>Abstract</p> <p>Background</p> <p>String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas.</p> <p>Results</p> <p>We propose new string kernels that combine the benefits of physico-chemical descriptors for amino acids with the ones of string kernels. The benefits of the proposed kernels are assessed on two problems: MHC-peptide binding classification using position specific kernels and protein classification based on the substring spectrum of the sequences. Our experiments demonstrate that the incorporation of amino acid properties in string kernels yields improved performances compared to standard string kernels and to previously proposed non-substring kernels.</p> <p>Conclusions</p> <p>In summary, the proposed modifications, in particular the combination with the RBF substring kernel, consistently yield improvements without affecting the computational complexity. The proposed kernels therefore appear to be the kernels of choice for any protein sequence-based inference.</p> <p>Availability</p> <p>Data sets, code and additional information are available from <url>http://www.fml.tuebingen.mpg.de/raetsch/suppl/aask</url>. Implementations of the developed kernels are available as part of the Shogun toolbox.</p
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