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A hybrid neural network model for noisy data regression

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The document presents a hybrid neural network model called GRNNFA, which combines fuzzy adaptive resonance theory (FA ART) and general regression neural network (GRNN) for effective noisy data regression. The model retains the fast training capabilities of both systems while reducing computational requirements. It employs FA for data compression and noise removal, optimizing kernel widths through a gradient-based algorithm. Experimental results demonstrate GRNNFA's effectiveness in various benchmark datasets and its application in predicting evacuation times during fire emergencies, showing superior performance compared to other models.

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How do you reduce noise in data?

Noisy data can be handled by missing value imputation ,outlier detection, data standardization normalization ,data encoding ,data validation, text processing, cross validation, feature sampling and ensemble methods. When tackling noisy data, understanding its source is crucial.

What is the best model for noisy data?

Hence, noisy data should be best modelled using ML models instead of traditional methods, such as splines. Cubic splines provide precise interpolation when the data have low noise or are noiseless; they are inaccurate when data is noisy.

What are the methods to handle noisy data?

The first step in handling noisy data is to identify it. You can use visualization tools like histograms, scatter plots, and box plots to detect outliers or anomalies in your dataset. Statistical methods such as z-scores can also help flag data points that deviate significantly from the mean.

Which regression for handling noisy data?

Handling noisy data in regression requires a multi-pronged approach. Preprocessing steps like outlier removal, data cleaning, and standardization are crucial. Choosing robust models like LASSO or Ridge regression, along with regularization techniques, helps combat overfitting.

Are neural networks good for regression tasks?

Neural networks consist of simple input/output units called neurons (inspired by neurons of the human brain). These input/output units are interconnected and each connection has a weight associated with it. Neural networks are flexible and can be used for both classification and regression.

Is KNN good for noisy data?

Like many other classifiers, k-NN classifier is noise-sensitive. Its accuracy highly depends on the quality of the training data. Noise and mislabeled data, as well as outliers and overlaps between data regions of different classes, lead to less accurate classification.

What is the best neural network model for temporal data?

Recurrent neural network or RNN: A recurrent neural network is capable of remembering the past the decisions are influenced by what it has learned in the past. In simple words, each node of a recurrent neural network acts as a memory cell while performing computations and carrying out operations.

What are the different types of neural networks for regression?

There are two main types of ANNs for regression analysis: feedforward and recurrent neural networks. Feedforward neural networks are the most commonly used type and are composed of layers of neurons that process the input data and produce the output.