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Topic Review
Matthews Correlation Coefficient
The Matthews correlation coefficient (MCC) or phi coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975. The MCC is defined identically to Pearson's phi coefficient, introduced by Karl Pearson, also known as the Yule phi coefficient from its introduction by Udny Yule in 1912. Despite these antecedents which predate Matthews's use by several decades, the term MCC is widely used in the field of bioinformatics and machine learning. The coefficient takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient between the observed and predicted binary classifications; it returns a value between −1 and +1. A coefficient of +1 represents a perfect prediction, 0 no better than random prediction and −1 indicates total disagreement between prediction and observation. However, if MCC equals neither −1, 0, or +1, it is not a reliable indicator of how similar a predictor is to random guessing because MCC is dependent on the dataset. MCC is closely related to the chi-square statistic for a 2×2 contingency table where n is the total number of observations. While there is no perfect way of describing the confusion matrix of true and false positives and negatives by a single number, the Matthews correlation coefficient is generally regarded as being one of the best such measures. Other measures, such as the proportion of correct predictions (also termed accuracy), are not useful when the two classes are of very different sizes. For example, assigning every object to the larger set achieves a high proportion of correct predictions, but is not generally a useful classification. The MCC can be calculated directly from the confusion matrix using the formula: In this equation, TP is the number of true positives, TN the number of true negatives, FP the number of false positives and FN the number of false negatives. If any of the four sums in the denominator is zero, the denominator can be arbitrarily set to one; this results in a Matthews correlation coefficient of zero, which can be shown to be the correct limiting value. The MCC can be calculated with the formula: using the positive predictive value, the true positive rate, the true negative rate, the negative predictive value, the false discovery rate, the false negative rate, the false positive rate, and the false omission rate. The original formula as given by Matthews was: This is equal to the formula given above. As a correlation coefficient, the Matthews correlation coefficient is the geometric mean of the regression coefficients of the problem and its dual. The component regression coefficients of the Matthews correlation coefficient are Markedness (Δp) and Youden's J statistic (Informedness or Δp'). Markedness and Informedness correspond to different directions of information flow and generalize Youden's J statistic, the [math]\displaystyle{ \delta }[/math]p statistics and (as their geometric mean) the Matthews Correlation Coefficient to more than two classes. Some scientists claim the Matthews correlation coefficient to be the most informative single score to establish the quality of a binary classifier prediction in a confusion matrix context.
  • 3.4K
  • 18 Nov 2022
Topic Review
Scunthorpe Problem
The Scunthorpe problem is the unintentional blocking of websites, e-mails, forum posts or search results by a spam filter or search engine because their text contains a string (or substring) of letters that appear to have an obscene or otherwise unacceptable meaning. Names, abbreviations, and technical terms are most often cited as being affected by the issue. The problem arises since computers can easily identify strings of text within a document, but interpreting words of this kind requires considerable ability to interpret a wide range of contexts, possibly across many cultures, which is an extremely difficult task. As a result, broad blocking rules may result in false positives affecting innocent phrases.
  • 3.3K
  • 14 Oct 2022
Topic Review
Limit Superior and Limit Inferior
In mathematics, the limit inferior and limit superior of a sequence can be thought of as limiting (i.e., eventual and extreme) bounds on the sequence. They can be thought of in a similar fashion for a function (see limit of a function). For a set, they are the infimum and supremum of the set's limit points, respectively. In general, when there are multiple objects around which a sequence, function, or set accumulates, the inferior and superior limits extract the smallest and largest of them; the type of object and the measure of size is context-dependent, but the notion of extreme limits is invariant. Limit inferior is also called infimum limit, limit infimum, liminf, inferior limit, lower limit, or inner limit; limit superior is also known as supremum limit, limit supremum, limsup, superior limit, upper limit, or outer limit. The limit inferior of a sequence [math]\displaystyle{ x_n }[/math] is denoted by The limit superior of a sequence [math]\displaystyle{ x_n }[/math] is denoted by
  • 3.3K
  • 30 Nov 2022
Topic Review
Battery Modelling Techniques
The battery modelling (BM) problem is a constrained, multi-dimensional, mixed variable, non-convex, non-linear optimisation problem. Many bio-inspired techniques have been successfully employed to estimate the battery parameters. When bio-inspired algorithms are implemented for COM to extract parameters in real time, then they are called grey box models.
  • 3.3K
  • 28 Oct 2021
Topic Review
Concept Learning
Concept learning, also known as category learning, concept attainment, and concept formation, is defined by Bruner, Goodnow, & Austin (1967) as "the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories". More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features. In a concept learning task, a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples. Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples. Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind.
  • 3.3K
  • 17 Oct 2022
Topic Review
Roadside Unit Deployment in Internet of Vehicles Systems
The network technology known as Internet of Vehicles (IoV) has been developed to improve road safety and vehicle security, with the goal of servicing the digital demands of car drivers and passengers. The highly dynamical network topology that characterizes these networks, and which often leads to discontinuous transmissions, is one of the most significant challenges of IoV. To address this issue, IoV infrastructure-based components known as roadside units (RSU) are designed to play a critical role by providing continuous transmission coverage and permanent connectivity. The main challenges that arise when deploying RSUs are balancing IoVs’ performances and total cost so that optimal vehicle service coverage is provided with respect to some target Quality of Service (QoS) such as: service coverage, throughput, low latency, or energy consumption.
  • 3.3K
  • 05 May 2022
Topic Review
Bitcoin Core
Bitcoin Core is free and open-source software that serves as a bitcoin node (the set of which form the bitcoin network) and provides a bitcoin wallet which fully verifies payments. It is considered to be bitcoin's reference implementation. Initially, the software was published by Satoshi Nakamoto under the name "Bitcoin", and later renamed to "Bitcoin Core" to distinguish it from the network. For this reason, it is also known as the Satoshi client. The MIT Digital Currency Initiative funds some of the development of Bitcoin Core. The project also maintains the cryptography library libsecp256k1.
  • 3.3K
  • 09 Nov 2022
Topic Review
Multi-Robot Systems
When applications require the integration and collaborative efforts of several robots, the collective of robots is referred to as a Multi-Robot System (MRS).
  • 3.3K
  • 17 Dec 2021
Topic Review
Graphical Projection
Graphical projection is a protocol, used in technical drawing, by which an image of a three-dimensional object is projected onto a planar surface without the aid of numerical calculation.
  • 3.3K
  • 07 Nov 2022
Topic Review
Video Games in Education
This page includes some history of video games being used as an additional or alternative method to traditional education. This page presents why using video games are beneficial to use for educational purposes in the classroom as well as the limitations. This page additionally discusses how learning from video games outside the classroom is possible as well.
  • 3.2K
  • 04 Nov 2022
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