649,970 research outputs found
Hybrid solutions to the feature interaction problem
In this paper we assume a competitive marketplace where the features are developed by different enterprises, which cannot or will not exchange information. We present a classification of feature interaction in this setting and introduce an on-line technique which serves as a basis for the two novel <i>hybrid</i> approaches presented. The approaches are hybrid as they are neither strictly off-line nor on-line, but combine aspects of both. The two approaches address different kinds of feature interactions, and thus are complimentary. Together they provide a complete solution by addressing interaction detection and resolution. We illustrate the techniques within the communication networks domain
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Deep sparse networks are widely investigated as a neural network architecture
for prediction tasks with high-dimensional sparse features, with which feature
interaction selection is a critical component. While previous methods primarily
focus on how to search feature interaction in a coarse-grained space, less
attention has been given to a finer granularity. In this work, we introduce a
hybrid-grained feature interaction selection approach that targets both feature
field and feature value for deep sparse networks. To explore such expansive
space, we propose a decomposed space which is calculated on the fly. We then
develop a selection algorithm called OptFeature, which efficiently selects the
feature interaction from both the feature field and the feature value
simultaneously. Results from experiments on three large real-world benchmark
datasets demonstrate that OptFeature performs well in terms of accuracy and
efficiency. Additional studies support the feasibility of our method.Comment: NeurIPS 2023 poste
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
There is currently a large gap in performance between the statistically
rigorous methods like linear regression or additive splines and the powerful
deep methods using neural networks. Previous works attempting to close this gap
have failed to fully investigate the exponentially growing number of feature
combinations which deep networks consider automatically during training. In
this work, we develop a tractable selection algorithm to efficiently identify
the necessary feature combinations by leveraging techniques in feature
interaction detection. Our proposed Sparse Interaction Additive Networks (SIAN)
construct a bridge from these simple and interpretable models to fully
connected neural networks. SIAN achieves competitive performance against
state-of-the-art methods across multiple large-scale tabular datasets and
consistently finds an optimal tradeoff between the modeling capacity of neural
networks and the generalizability of simpler methods
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