Advantages and Disadvantages of ANN in Data Mining
Neural networks are a series of algorithms that act like a human brain to recognize patterns and among vast amounts of data. An artificial neural network (ANN) also referred to as simply a “Neural Network” (NN), could be a process model supported by biological neural networks. It consists of an interconnected collection of artificial neurons. A neural network is a set of connected input/output units where each connection has a weight associated with it. During the knowledge phase, the network acquires by adjusting the weights to be able to predict the correct class label of the input samples. Neural network learning is also denoted as connectionist learning, due to the connections between units. Neural networks involve long training times and are therefore more appropriate for applications where this is feasible. They require a number of parameters that are typically best determined empirically, such as the network topology or “structure”. Neural networks have been criticized for their poor interpretability, since it is difficult for humans to take the symbolic meaning behind the learned weights. These features firstly made neural networks less desirable for data mining.
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Advantages of Artificial Neural Networks
- Artificial neural networks have the ability to provide the data to be processed in parallel, which means they can handle more than one task at the same time.
- Artificial neural networks have been in resistance. This means that the loss of one or more cells, or neural networks, influences the performance of Artificial Neural networks.
- Artificial neural networks are used to store information on the network so that, even in the absence of a data pair, it does not mean that the network is not generating results.
- Artificial neural networks are gradually being broken down, which means that they will not suddenly stop working and these networks are gradually being broken down.
- We are able to train ANN’s that these networks learn from past events and make decisions.
Disadvantages of Artificial Neural Networks
- As we mentioned before, with ANN arms hanging along with the execution of parallel processing, and so they need processors that support parallel processing, so the ANNs are dependent on the hardware.
- Since it’s similar to the functionality of the human brain, we may not be able to determine what is the proper network structure of an Artificial Neural network.
- Not only do artificial neural networks, but also the statistical models can be trained with only numeric data, so it makes it very difficult for ANN to understand the problem statement.
- When an artificial neural network that provides a solution to the problem statements that we really don’t know on what basis it will give the solution, and this time, ANN is not a reliable