**1. Introduction**

With the growing amount of data produced daily, the need of techniques to handle these data has been increased. Machine learning (ML) is a prominent area of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence (AI) [1]. ML is algorithms that are able to learn from data, identify patterns in observed data, and build models that make predictions about unseen data. Algorithm or model enables

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

computer (machine) to learn from data. Over the past decades, learning algorithms have found widespread applications in numerous areas such as computer vision, object recognition, web search, natural language processing, emotion recognition, etc. The performance of different ML algorithms strongly depends on the size and structure of dataset of the domain.

Researchers have shown that active learning is beneficial in many domains [9], including edu-

Human Active Learning

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http://dx.doi.org/10.5772/intechopen.81371

Semi-supervised learning (or SSL) has attracted a highly considerable amount of interest in ML. SSL techniques allow classifiers or learners to learn from labeled and unlabeled data at the same time [13, 14]. Typically, they are used when we have a small-size labeled dataset with a large-size unlabeled dataset. **Figure 2** intuitively shows the difference between supervised and semi-supervised learning. Actually most real-world learning scenarios are SSL. During the last two decades, SSL methods such as active learning, co-training, and co-testing have significantly improved learning performance in various

When a machine learning model is trained, learning is performed on a random subset of all the available sets of labeled training data. We will refer to this mode of learning as passive learning (PL). In PL mode of learning, the classifier (learner) does not participate interactively with the

cation [10], machine learning [5], remote sensing [11], and cognitive science [12].

**2. Active machine learning**

**Figure 2.** Supervised vs. semi-supervised learning.

applications.

Supervised machine learning learns from the available data (experience), which is given in the form of training data (instances). The knowledge induced from the data can then be used for descriptive or predictive purposes. Supervised learning problems can be categorized as either classification or regression, depending on the output label of the data [2]. Classification problems assign a discrete class label to input instance, while regression problems have continuous numeric values. Classification is a function that assigns a new object (or instance) as belonging to one of the predefined classes [1]. The goal of classification is to accurately predict the target class for each instance in the data.

**Figure 1** shows the workflow in supervised learning. We can see that there are two different steps: training and prediction. During training, a feature extractor is used to convert each input (training data instance) to a feature set. These feature sets with labels are fed into the learning algorithm to generate a classifier model. During prediction, the same feature extractor is used to convert unseen inputs to feature sets that are then fed into the model, which generates predicted labels [3, 4].

ML with many other disciplines of AI are gaining popularity, and they have been used in numerous fields and industries, including finance, healthcare, education, and psychology. Since learning is an important aspect of intelligent behavior, ML can be used commonly for data analysis in psychology and cognitive science. Recently, ML methods have been investigated in experimental psychology and human categorization.

In ML, active learning refers to an approach that selects the queries (instances) for labeling from a large pool of unlabeled data [5, 6]. In most cases, an active learning algorithm outperformed random sampling method and reduced number of instances that necessary to achieve similar performance. Active learning is often used for problems where it is difficult (expensive and/or time-consuming) to obtain labeled training data [7, 8].

**Figure 1.** The architecture of a supervised classification.

Researchers have shown that active learning is beneficial in many domains [9], including education [10], machine learning [5], remote sensing [11], and cognitive science [12].
