We introduce a novel nonlinear model, Sparse Adaptive Bottleneck
Centroid-Encoder (SABCE), for determining the features that discriminate
between two or more classes. The algorithm aims to extract discriminatory
features in groups while reconstructing the class centroids in the ambient
space and simultaneously use additional penalty terms in the bottleneck layer
to decrease within-class scatter and increase the separation of different class
centroids. The model has a sparsity-promoting layer (SPL) with a one-to-one
connection to the input layer. Along with the primary objective, we minimize
the $l_{2,1}$-norm of the sparse layer, which filters out unnecessary features
from input data. During training, we update class centroids by taking the
Hadamard product of the centroids and weights of the sparse layer, thus
ignoring the irrelevant features from the target. Therefore the proposed method
learns to reconstruct the critical components of class centroids rather than
the whole centroids. The algorithm is applied to various real-world data sets,
including high-dimensional biological, image, speech, and accelerometer sensor
data. We compared our method to different state-of-the-art feature selection
techniques, including supervised Concrete Autoencoders (SCAE), Feature
Selection Networks (FsNet), Stochastic Gates (STG), and LassoNet. We
empirically showed that SABCE features often produced better classification
accuracy than other methods on the sequester test sets, setting new
state-of-the-art results.

本文提出一种新颖的非线性模型，Sparse Adaptive Bottleneck Centroid-Encoder（简称 SABCE），用于确定区分两个或更多类别之间特征的算法，并将其应用于各种实际数据集中，包括高维生物，图像，语音和加速度计传感器数据，经实验证明，SABCE 所提取的特征通常比其他方法产生更好的分类准确性，创造了新的最佳结果。

利用基于稀疏自适应瓶颈中心编码器的特征选择

Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder

Many different machine learning algorithms exist; taking into account each
algorithm's hyperparameters, there is a staggeringly large number of possible
alternatives overall. We consider the problem of simultaneously selecting a
learning algorithm and setting its hyperparameters, going beyond previous work
that addresses these issues in isolation. We show that this problem can be
addressed by a fully automated approach, leveraging recent innovations in
Bayesian optimization. Specifically, we consider a wide range of feature
selection techniques (combining 3 search and 8 evaluator methods) and all
classification approaches implemented in WEKA, spanning 2 ensemble methods, 10
meta-methods, 27 base classifiers, and hyperparameter settings for each
classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup
09, variants of the MNIST dataset and CIFAR-10, we show classification
performance often much better than using standard selection/hyperparameter
optimization methods. We hope that our approach will help non-expert users to
more effectively identify machine learning algorithms and hyperparameter
settings appropriate to their applications, and hence to achieve improved
performance.

使用贝叶斯优化和特征选择技术，全自动选择机器学习算法和设置其超参数，在 21 个数据集的分类表现中达到了比标准选择 / 超参数优化方法更好的结果。

Auto-WEKA：分类算法的组合选择与超参数优化

Auto-WEKA: Combined Selection and Hyperparameter Optimization of  Classification Algorithms

We present memory-based learning approaches to shallow parsing and apply
these to five tasks: base noun phrase identification, arbitrary base phrase
recognition, clause detection, noun phrase parsing and full parsing. We use
feature selection techniques and system combination methods for improving the
performance of the memory-based learner. Our approach is evaluated on standard
data sets and the results are compared with that of other systems. This reveals
that our approach works well for base phrase identification while its
application towards recognizing embedded structures leaves some room for
improvement.

研究了基于记忆的学习方法在浅层解析中的应用，并探讨了五项任务的性能改进，实验结果表明该方法在基础短语识别中表现良好，但对嵌套结构的认知还有待改进。