ID 原文 译文
1283 在线流特征选择通过实时过滤无关特征和冗余特征，实现流特征空间降维。 Online streaming feature selection achieves stream feature space dimensionality reduction by filtering irrele-vant features and redundant features in real time.
1284 针对已有算法，如 Alpha-in-vesting 分类精度低、SAOLA 选择特征数多和 OSFS 在低冗余高相关数据集下运行时间长的问题， Existing works, such as Alpha-investing and Online Streaming Feature Se-lection (OSFS), have been proposed to serve this purpose, but they have drawbacks, including low prediction accuracy and high running time if the streaming features exhibit characteristics such as low redundancy and high relevance.
1285 提出了一种面向分类的流特征在线特征选择算法———OSFIC。 We propose anovel classification-oriented online feature selection algorithm for streaming features, named OSFIC.
1286 算法运用四层过滤框架，通过无条件独立过滤不相关新特征、单条件下互信息过滤冗余新特征和候选特征集合中的部分冗余特征， OSFIC uses a four-layer filtering framework to filter irrelevant new features by null-conditional independence, filter redundant new features and re-dundant features in a candidate feature set by a single-conditional mutual information,
1287 最后通过多条件独立过滤候选特征集中的剩余冗余特征，最终得到分类标签的近似马尔可夫毯。 and finally filter the remaining redun-dancy in the candidate feature set by multi-conditional independence. The approximate Markov blanket of the classify label isfinally obtained.
1288 为了分析 OSFIC 的性能，选择了 NIPS 2003 和 Causality Workbench 中的数据集，从预测精度、特征数量、运行时间和 AUC 方面与已有基准算法进行比较。 To analyze the performance of the algorithm, we selected the datasets in NIPS 2003 and Causality Work-bench to compare prediction accuracy, number of selected features, runtime, and AUC with existing state-of-the-art algo-rithms.
1289 实验表明，OSFIC 平均分类精度比 Alpha-investing提升 4.41% 。 Experiments show that the average classification accuracy of OSFIC is 4. 41% higher than that of Alpha-investing.
1290 在保证精度的前提下，平均特征数量比 SAOLA 减少 41.9% ，运行时间比 OSFS 减少 91.59% 。 Under the premise of high precision, the average number of features is 41. 9% lower than SAOLA, and the runtime is 91. 59% lower than OSFS.
1291 最后，在真实的应用场景下验证了 OSFIC 的有效性。 Finally, the efficiency of OSFIC is verified in real scenarios.
1292 目标检测作为计算机视觉的一个重要研究方向，近年来在算法性能上有了突破性进展。 Object detection is an important research direction in the field of computer vision. In recent years, object detection has made great advances in public datasets, and there are also breakthroughs in algorithmic performance.