Imbalanced classification is a well-known challenge faced by many real-world
applications. This issue occurs when the distribution of the target variable is
skewed, leading to a prediction bias toward the majority class. With the
arrival of the Big Data era, there is a pressing need for efficient solutions
to solve this problem. In this work, we present a novel resampling method
called SMOTENN that combines intelligent undersampling and oversampling using a
MapReduce framework. Both procedures are performed on the same pass over the
data, conferring efficiency to the technique. The SMOTENN method is
complemented with an efficient implementation of the neighborhoods related to
the minority samples. Our experimental results show the virtues of this
approach, outperforming alternative resampling techniques for small- and
medium-sized datasets while achieving positive results on large datasets with
reduced running times.

引入了一种名为 SMOTENN 的新型重采样方法，它通过使用 MapReduce 框架将智能欠采样和过采样结合在一起，从而解决了不平衡分类问题。实验结果表明，该方法在小型和中型数据集上优于替代重采样技术，并在大型数据集上达到了良好的效果且运行时间缩短。

高效的混合过采样和智能欠采样用于不平衡大数据分类

Efficient Hybrid Oversampling and Intelligent Undersampling for  Imbalanced Big Data Classification

Stochastic Programming is a framework for modelling and solving problems of
decision making under uncertainty. Stochastic Dynamic Programming is a branch
of Stochastic Programming that takes a "functional equation" approach to the
discovery of optimal policies. By leveraging constructs - lambda expressions,
functional interfaces, collections and aggregate operators - implemented in
Java to operationalise the MapReduce framework, jsdp provides a general purpose
library for modelling and solving Stochastic Dynamic Programs.

Stochastic Dynamic Programming 在处理不确定决策时采用函数方程方法，而 jsdp 实现了一种基于 Java 的 MapReduce 框架的泛用库用于建模和解决问题。

jsdp：Java 随机动态规划库

jsdp: a Java Stochastic Dynamic Programming Library

Many techniques have been proposed to implement the Apriori algorithm on
MapReduce framework but only a few have focused on performance improvement. FPC
(Fixed Passes Combined-counting) and DPC (Dynamic Passes Combined-counting)
algorithms combine multiple passes of Apriori in a single MapReduce phase to
reduce the execution time. In this paper, we propose improved MapReduce based
Apriori algorithms VFPC (Variable Size based Fixed Passes Combined-counting)
and ETDPC (Elapsed Time based Dynamic Passes Combined-counting) over FPC and
DPC. Further, we optimize the multi-pass phases of these algorithms by skipping
pruning step in some passes, and propose Optimized-VFPC and Optimized-ETDPC
algorithms. Quantitative analysis reveals that counting cost of additional
un-pruned candidates produced due to skipped-pruning is less significant than
reduction in computation cost due to the same. Experimental results show that
VFPC and ETDPC are more robust and flexible than FPC and DPC whereas their
optimized versions are more efficient in terms of execution time.

本研究提出了改进的基于 MapReduce 的 Apriori 算法 VFPC 和 ETDPC，以及优化后的 Optimized-VFPC 和 Optimized-ETDPC，实验结果表明 VFPC 和 ETDPC 比 FPC 和 DPC 更强大，更灵活，而它们的优化版本在执行时间方面更加高效。

基于 Hadoop 集群的 MapReduce Apriori 算法性能优化

Performance Optimization of MapReduce-based Apriori Algorithm on Hadoop  Cluster

Data originating from the Web, sensor readings and social media result in
increasingly huge datasets. The so called Big Data comes with new scientific
and technological challenges while creating new opportunities, hence the
increasing interest in academia and industry. Traditionally, logic programming
has focused on complex knowledge structures/programs, so the question arises
whether and how it can work in the face of Big Data. In this paper, we examine
how the well-founded semantics can process huge amounts of data through mass
parallelization. More specifically, we propose and evaluate a parallel approach
using the MapReduce framework. Our experimental results indicate that our
approach is scalable and that well-founded semantics can be applied to billions
of facts. To the best of our knowledge, this is the first work that addresses
large scale nonmonotonic reasoning without the restriction of stratification
for predicates of arbitrary arity. To appear in Theory and Practice of Logic
Programming (TPLP).

本文提出了一种基于 MapReduce 框架的并行化方法，探讨了基于良基的语义如何处理海量数据，并指出实验结果表明其可扩展性好，具有应用于数十亿条事实的潜力。此外，这是第一篇涉及任意元谓词的大规模非单调推理的研究。