In machine learning, the closely related problem of multilabel classification and multi-label classification are variations of the same classification problem where multiple labels can be assigned to a single instance. The training in both areas is practically the same but the methods of handling data in each area are quite different. Training in a reinforcement training scheme, where an output is the result of a given decision and whether the result is good or bad, is used in multilabel classification while in a traditional classification task (where a label indicates a variable), a more specific decision criterion is used for each label. This criterion is normally a function of the weights for the categories in the unsupervised portion of the classification. Thus, the main difference between these two systems lies in the implementation of training in terms of the weights in addition to the traditional function of a classifier determining if an instance is worth a place in the training data set.
One important point in both approaches is that the weights should be non-zero in the decision function so as to eliminate any possibility of false results due to misclassification. The traditional decision criterion for each category is typically some kind of arithmetic mean, while in multilevel decision trees, the weights for each category are chosen using a Monte Carlo simulation. Both approaches have excellent results but the former are more precise, especially when dealing with large databases. But the latter has the nice property that it can be used on any size data set while preserving the originality of the training data set. The classification accuracy can thus be increased by introducing more labels into the system.
There are many ways in which multilabel classification algorithms can be implemented. Tree based multilabel classification schemes have gained considerable popularity. The advantage of these decision trees is that they can be easily implemented and they form a very good starting point for more complex classification problems, where the number of categories is usually greater than the number of instances. The main drawback is that they require a significant amount of memory and they are not as flexible as other multilabel classification algorithms.
Another approach taken is to use random classifiers. In this case, the machine is allowed to select multilabel classification candidates from among some of their inputs. This means that the machine can learn from its own mistakes, unlike the traditional approach. The disadvantage is that it tends to become ineffective as the number of examples increases. On the other hand, this method is much more flexible and much easier to implement on a wide variety of inputs.
Another approach taken is to apply supervised learning techniques to the problem. In this technique, a multilabel classification task is given to a network of classifiers. The network can be trained using some kind of reinforcement or simply by showing it some inputs which it can recognize. Once the network has a good understanding of how to classify data, it can simply use this knowledge to make an educated guess at the classifier’s output. While this may seem like an oversimplification, it is a clear example of how the classification algorithm influences the decision making process. By changing the definition of what “class” means, we can in effect change the way a system will classify.
One more classification algorithm relies only on some form of statistical information. One popular example of this is a neural network. This type of classification usually consists of one or more hidden layers of “neural cells” which are connected through hundreds or thousands of connections. The network’s output is then the output of the connections. The neurons in this particular network are very complex and so the final classification can be called “surprisingly accurate” (given the noisy nature of these types of networks). These networks are still quite useful, because they do not have to take into account the unsupervised feature of multilevel data.
Another important feature of an accurate classification algorithm is that it must be able to deal with multiple types of labels. Sometimes an individual data set contains a number of different classes. And sometimes labels are used to tell the user something about the data that was not part of the original training data set. These cases, such as missing data or erroneous labels, often pose problems for traditional classification methods.
Fortunately, there is a multilabel classification method that takes care of all these problems. A newer method called WordNet recently received high praise from many IT professionals, because it is capable of representing data from multiple sources without labels. This results in a much better classification method than anything that existed before. This is the kind of highly efficient classification method that every business should be using.
