An Overview of Multilabel Classifications

We are very familiar with the single-label classification problems. We mostly come across binary and multiclass classifications. But, with the increasing applications of machine learning, we face different problems like movie genre classifications, medical report classification, and text classification according to some given topics. These problems can’t be addressed using single-label classifiers, as an instance may belong to several classes or labels at the same time. For instance, a movie can be of Action and Adventure genre at the same time. This is where multilabel classification steps in. In the article, we will go through some common approaches to handle multilabel problems, and in later parts also look at some applications.

Before we jump into approaches, let’s take a look at the metrics used in such problems.

Metrics

In binary or multiclass classification, we normally use Accuracy as our main evaluation metrics and additionally F1 score and ROC measures. In multilabel classification, we need different metrics because there is a chance that the results are partially correct or fully correct as we are having multiple labels for a record in a dataset.

Depending on the problem there are 3 main types of metrics classes:

a. Evaluating Partitions

b. Evaluating Ranking

c. Using label Hierarchy

Partitions

To capture the partial correctness, this strategy works on the average difference between the actual label and the predicted label. We pick up samples from our dataset and predict those samples using a model. We obtain the difference in actual and predicted labels of the samples one at a time and then find the average differences over all samples. This approach is called Example-Based Evaluation.

Another method is predicting all the test-data set, and evaluating the difference label-wise, i.e, considering each label of the result as a single vector and finding the difference between the predicted values and the actual values for a particular label. Once we find the difference for each label. We take an average of the errors. This is called Label-Based Evaluation.

So, we can say Example-based is a row or sample wise approach to find the difference, it treats the all the predicted labels of a sample as a whole, finds the difference for one sample at a time, whereas, for the Label-based, it is a column or label wise approach, it treats each label as a whole, i.e, the values of all the samples for that particular label is considered. We find the difference between the predicted and actual value for that label and average overall labels.

This approach of Label-based to treat each label independently fails to address the correlations among the different class labels.

#machine-learning #artificial-intelligence #classification #multilabel-classifier #deep learning

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An Overview of Multilabel Classifications

We are very familiar with the single-label classification problems. We mostly come across binary and multiclass classifications. But, with the increasing applications of machine learning, we face different problems like movie genre classifications, medical report classification, and text classification according to some given topics. These problems can’t be addressed using single-label classifiers, as an instance may belong to several classes or labels at the same time. For instance, a movie can be of Action and Adventure genre at the same time. This is where multilabel classification steps in. In the article, we will go through some common approaches to handle multilabel problems, and in later parts also look at some applications.

Before we jump into approaches, let’s take a look at the metrics used in such problems.

Metrics

In binary or multiclass classification, we normally use Accuracy as our main evaluation metrics and additionally F1 score and ROC measures. In multilabel classification, we need different metrics because there is a chance that the results are partially correct or fully correct as we are having multiple labels for a record in a dataset.

Depending on the problem there are 3 main types of metrics classes:

a. Evaluating Partitions

b. Evaluating Ranking

c. Using label Hierarchy

Partitions

To capture the partial correctness, this strategy works on the average difference between the actual label and the predicted label. We pick up samples from our dataset and predict those samples using a model. We obtain the difference in actual and predicted labels of the samples one at a time and then find the average differences over all samples. This approach is called Example-Based Evaluation.

Another method is predicting all the test-data set, and evaluating the difference label-wise, i.e, considering each label of the result as a single vector and finding the difference between the predicted values and the actual values for a particular label. Once we find the difference for each label. We take an average of the errors. This is called Label-Based Evaluation.

So, we can say Example-based is a row or sample wise approach to find the difference, it treats the all the predicted labels of a sample as a whole, finds the difference for one sample at a time, whereas, for the Label-based, it is a column or label wise approach, it treats each label as a whole, i.e, the values of all the samples for that particular label is considered. We find the difference between the predicted and actual value for that label and average overall labels.

This approach of Label-based to treat each label independently fails to address the correlations among the different class labels.

#machine-learning #artificial-intelligence #classification #multilabel-classifier #deep learning

A Classification Project in Machine Learning: a gentle step-by-step guide

Classification is a core technique in the fields of data science and machine learning that is used to predict the categories to which data should belong. Follow this learning guide that demonstrates how to consider multiple classification models to predict data scrapped from the web.

#overviews #classification #machine learning

How to Use One-Vs-Rest and One-Vs-One for Multi-Class Classification

Only a few classification models aid multi-class classification. Specific algorithms, including logistic regression and perceptron, work best with binary classification and do not support more than two classes of classification tasks. The best alternative for solving multi-class classification problems is splitting the multi-class datasets into multiple binary assemblies of data that can fit the binary classification model.

Algorithms used in binary classification problems cannot work with multi-class tasks. Therefore, heuristic methods, such as one-vs-one and one-vs-rest, are used to split multi-class problems into multiple binary datasets and train the binary classification model.

Binary vs. Multi-Class Classification

Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.

#overviews #classification #machine learning #data-science

Feature pyramid network for image classification

Object detection is one of the main problems in computer vision that may fail when there are multi-scale objects in images. Using feature pyramids helps to solve this problem.

Some previous studies tried to use different kinds of feature pyramids to improve object detection. One method fed various sizes of the input image to the deep network to see objects with different scales. This way also helped improve object detection but increases computational costs and processing time so much that it is not efficient.

Feature pyramid network(FPN) was introduced by Tsung-Yi Lin et al., which enhanced object detection accuracy for deep convolutional object detectors. FPN solves this problem by generating a bottom-up and a top-down feature hierarchy with lateral connections from the network’s generated features at different scales. This helps the network generate more semantic features, so using FPN helps increase detection accuracy when there are objects with various scales in the image while not changing detection speed.

_Here, I aim to introduce a new architecture based on FPN to improve classification accuracy. This architecture is proposed in my _paper.

As described, FPN helps extract multi-scale features from the input image, which better presents objects with different scales. We have designed an architecture that utilizes FPN to understand better the important parts of the image that could exist in different sizes.

In the next figure, you can see our proposed architecture. This architecture was developed for classifying the patient CT scan images into normal and COVID-19. Researchers can modify this architecture for using on different datasets and classes.

#image-classification #neural-networks #classification #deep-learning #machine-learning

A Quick Overview of Metacat API for Discoverable Big Data

Introduction to Metacat API

MetacatAPI is an Application Programming Interface provides an interface and helps connect the two applications and enable them to communicate with each other. Whenever we use an application or send a request, the application connects to the internet and sends the request to the server, and the server then, in response, provides the fetched data.

The data set at any organization is stored in different data warehouses like Amazon S3 (via Hive), Druid, Elasticsearch, Redshift, Snowflake, and MySql.Spark, Presto, Pig, and Hive are used to consume, process, and produce data sets. Due to numerous data sources and to make the data platform interoperate and work as a single data warehouse Metacat was built. Metacat is a metadata exploration API service. Metadata can be termed as the data about the data. Metacat explores Metadata present on Hive, RDS, Teradata, Redshift, S3, and Cassandra.

#big data engineering #blogs #a quick overview of metacat api for discoverable big data #metacat api #discoverable big data #overview