What Is Neural Network Classification? Techniques and Applications

You can use neural network classification to automatically classify data into categories. For example, if you had a database of images of pets, a neural network classification model could sort those images into pictures of dogs and cats. You can use classification in many different ways, such as classifying social media posts about your company, or brand, by sentiment to understand both the positive and the negative comments made about it online. You could also use classification algorithms to predict movements of the stock market by analyzing patterns of positive and negative movement. Classification allows the AI model to categorize data to understand the pattern and make predictions. If working in the field of artificial intelligence interests you, consider a career as a neural network engineer; according to Glassdoor, you could potentially earn a median annual salary of $102,963 [1].

Explore types of classification and neural network classification algorithms, as well as applications of this technology in the real world. 

What is neural network classification?

Neural network classification is using an algorithm within a neural network to sort data into categories, also called classes. Initially, you use a set of training data to teach the neural network and sharpen the algorithm regarding the classification of various items. Once you accomplish this, you expose the neural network to a new data set that it can classify according to the established categories. The neural network can then finish categorizing speech and image recognition data in minutes rather than the hours it might take for you to do it. A platform that uses neural networks that you might use daily is the Google search algorithm. 

You can use neural network algorithms for various duties such as classification, prediction, clustering, and associating. Today, you can utilize these algorithms to address different issues, a few examples being bioinformatics, drug design, natural language translation, and social media filtering. Regarding the classification issue you’re trying to resolve, you can separate the classification-based predictive modeling approach into different types. Three methods of classification are binary classification, multiclass classification, and multilabel classification. Continue reading to discover more about these three techniques of classification. 

How do classification models work?

Neural network classification models work by training. You must provide a neural network with a clean set of training materials. The model needs to learn before it can classify. You can either label the data and pre-define the categories for the neural network to sort, known as supervised learning, or use unsupervised or semi-supervised learning to train the network to recognize patterns and categorize data accordingly. In the training process, the model learns to identify objects within an image or words within a block of text. The model can note the features of each object or word and compare them against the provided class to learn what features to expect from objects and words in each category. 

After you finish the training, you can test the algorithm to make sure it’s properly balanced to analyze new data. To accomplish this, you can use test data. This is data from the training materials kept aside so it’s new to the AI model, but you can still use it to accurately judge whether the model is working properly. If the AI model successfully categorizes the test data, it will be ready to analyze data sets you and it has never seen before. 

Types of classification 

You can choose from different methods and techniques for classification, like binary or multiclass, depending on your needs. As you read above, you can also choose from different types of learning such as supervised or unsupervised. In supervised learning, you provide the neural network with labeled training data. In unsupervised learning, you allow the AI model to determine its categories after exploring data. Semi-supervised learning combines these techniques. Meanwhile, reinforcement learning uses a slightly different strategy that has varying levels of labeled data. You can also use different techniques to classify data, including: 

• Binary classification: This technique sorts data into one of two categories. For example, the spam filter on your email is a binary classification. After learning the pattern of what a spam email looks like, the algorithm evaluates each incoming email and determines whether it is likely to be spam with a simple yes or no answer. Binary classification can sort for any feature or characteristic that you can phrase as a yes/no or true/false question. 

• Multiclass classification: With a multiclass classification, you can sort data into more than one category. For example, you might sort a data set of nature images into a set of locations such as “beach,” “mountains,” “river,” “forest,” and “desert”. Or, to expand on the email filtering example, a multiclass model could also consider whether emails belong in a social or promotional inbox. 

• Multilabel classification: Multilabel classification is a technique for labeling data that can exist in more than one category. For example, a library could use a multilabel classification algorithm to sort books by genre and author, which would require books to appear in at least two classes. 

Classification algorithms

You can decide from several types of classification algorithms, like logistic regression or decision tree. Each algorithm is suited to a different type of task. Although, in many cases, you can use more than one type to complete the work you have in mind. A few popular algorithms for classification include: 

• Logistic regression: Logistic regression is an algorithm that estimates the probability of an event. In the case of neural network classification, a logistic regression predicts the probability that data will fall into one class or another based on its features. Logistic regression makes its prediction using a mathematical equation called linear regression. 

• K-nearest neighbor: K-nearest neighbor (KNN) is an algorithm that classifies data by plotting data points representing variables in the data. The algorithm makes a decision about what class data belongs to based on what data points are its nearest neighbors. K-nearest neighbors assume that variables that plot near one another are similar, and therefore represent objects belonging to the same class. KNN is also known as the “guilty by association” algorithm.

• Decision tree: A decision tree is an algorithm that predicts which class data belongs to by making a series of decisions about it. You may be familiar with other forms of decision trees where you answer a series of “yes or no” questions about a topic, which leads you through a map of potential answers until you arrive at the correct one. This algorithm works similarly. For example, if you were to sort pictures of animals into a multiclass classification system using a decision tree, the AI model might ask itself questions about the features of the image until it decides what animal the image depicts. 

• Support vector machine (SVM): SVMs perform binary classification by finding plot points for variables in the data and finding a hyperplane that maximizes space between two classes. For example, if you were sorting between black shapes and white shapes, the AI model would plot the value of each color in the data set. Objects with black color values would appear in one group on the graph, while objects with white colors would appear in another. SVM draws a line—the hyperplane—between the two groups in a way that maximizes the distance between both groups, thereby predicting the most appropriate place to divide the two classes. Establishing the hyperplane boundary helps the model determine in which class to place future pieces of data. 

• Naive Bayes: Naive Bayes is a classification algorithm that utilizes the fundamentals of probability to classify the data, and it is implemented as a method for solving classification problems. Text classification is a common use for this algorithm, a few examples being analyzing sentiment, categorizing new articles, and filtering spam. Rather than employing simple feature recognition, Naive Bayes absorbs the probability of every object and its features as well as the groups where you would place them. Based on this information, it then predicts the likelihood regarding which group to classify the data. 

• Random forest: A random forest algorithm refers to a set of decision trees. This algorithm classifies data by allowing each individual decision tree to make a prediction about what class to put the data in. After all of the algorithms have calculated their response, they vote on the answer and the decision of the majority becomes the output. 

Learn more about neural network classification on Coursera

Neural network classification is a machine learning technique that allows AI models to sort data into classes. If you want to learn more about neural networks or explore career options in machine learning, you can begin today on Coursera. Consider enrolling in an online course such as Supervised Machine Learning: Regression and Classification offered by DeepLearning.AI as part of the Machine Learning Specialization offered by DeepLearning.AI in partnership with Stanford. Or, consider IBM’s Supervised Machine Learning: Classification offered as part of both the IBM Machine Learning Professional Certificate and the IBM Introduction to Machine Learning Specialization.