对等节点的机器学习算法
我想将机器学习应用于并行环境中的分类问题。多个独立节点（每个节点具有多个开/关传感器）可以传送其传感器数据，目的是按照启发式、训练数据或两者的定义对事件进行分类。
每个对等点将从其独特的角度测量相同的数据，并尝试对结果进行分类，同时考虑到任何相邻节点（或其传感器或仅与节点的连接）可能出现故障。节点应该发挥平等的作用，并通过传达结果来确定最可能的分类。
最终，每个节点都应该根据自己的传感器数据和同行的数据做出决定。如果重要的话，对于某些分类来说，误报是可以的（尽管这是不可取的），但误报是完全不可接受的。
考虑到每个最终分类都会收到好的或坏的反馈，如果节点可以相互通信以确定最有可能的分类，那么解决此问题的合适机器学习算法是什么？
I want to apply machine learning to a classification problem in a parallel environment. Several independent nodes, each with multiple on/off sensors, can communicate their sensor data with the goal of classifying an event as defined by a heuristic, training data or both.
Each peer will be measuring the same data from their unique perspective and will attempt to classify the result while taking into account that any neighbouring node (or its sensors or just the connection to the node) could be faulty. Nodes should function as equal peers and determine the most likely classification by communicating their results.
Ultimately each node should make a decision based on their own sensor data and their peers' data. If it matters, false positives are OK for certain classifications (albeit undesirable) but false negatives would be totally unacceptable.
Given that each final classification will receive good or bad feedback, what would be an appropriate machine learning algorithm to approach this problem with if the nodes could communicate with each other to determine the most likely classification?
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如果每个单独节点中的传感器数据通常足以做出合理的决定，那么它们可以仅传达结果并进行多数投票。如果多数投票不合适，您可以训练一个额外的分类器，该分类器使用节点的输出作为其特征向量。
由于您希望进行带反馈的在线监督学习，因此您可以使用具有反向传播功能的神经网络或增量支持向量机，将误差添加到训练集中。研究分类器偏差以处理假阳性/假阴性权衡。
If the sensor data in each individual node is generally sufficient to make a reasonable decision, they could just communicate the result and take a majority vote. If majority vote is not appropriate, you could train an additional classifier that uses the outputs of the nodes as its feature vector.
Since you want to have on-line supervised learning with feedback, you could use a neural network with backpropagation or an incremental support vector machine that adds the errors to the training set. Look into classifier biasing to deal with false-positive/false-negative trade-off.
在这种情况下，神经网络可能非常合适。网络的输入将是节点上的每个传感器及其邻居的传感器。您将根据您的反馈计算权重。
另一种选择（更简单，但也可以获得良好的结果）是 八卦算法。不过，您必须考虑纳入反馈。
In this instance, a neural network could be very appropriate. The inputs to the network would be each of the sensors onboard the node, along with that of its neighbors. You would calculate weights based on your feedback.
Another option (that is simpler, but can achieve good results as well) is a Gossip Algorithm. You would have to look into incorporating feedback though.