Random forestRandom forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees' habit of overfitting to their training set.
Statistical classificationIn statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.
Binary classificationBinary classification is the task of classifying the elements of a set into two groups (each called class) on the basis of a classification rule. Typical binary classification problems include: Medical testing to determine if a patient has certain disease or not; Quality control in industry, deciding whether a specification has been met; In information retrieval, deciding whether a page should be in the result set of a search or not. Binary classification is dichotomization applied to a practical situation.
Naive Bayes classifierIn statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.
Bootstrap aggregatingBootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
Support vector machineIn machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
Linear classifierIn the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector.
SeizureAn epileptic seizure, informally known as a seizure, is a period of symptoms due to abnormally excessive or synchronous neuronal activity in the brain. Outward effects vary from uncontrolled shaking movements involving much of the body with loss of consciousness (tonic-clonic seizure), to shaking movements involving only part of the body with variable levels of consciousness (focal seizure), to a subtle momentary loss of awareness (absence seizure). Most of the time these episodes last less than two minutes and it takes some time to return to normal.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
EpilepsyEpilepsy is a group of non-communicable neurological disorders characterized by recurrent epileptic seizures. An epileptic seizure is the clinical manifestation of an abnormal, excessive, purposeless and synchronized electrical discharge in the brain cells called neurons. The occurrence of two or more unprovoked seizures defines epilepsy. The occurrence of just one seizure may warrant the definition (set out by the International League Against Epilepsy) in a more clinical usage where recurrence may be able to be prejudged.
Seizure typesIn the field of neurology, seizure types are categories of seizures defined by seizure behavior, symptoms, and diagnostic tests. The International League Against Epilepsy (ILAE) 2017 classification of seizures is the internationally recognized standard for identifying seizure types. The ILAE 2017 classification of seizures is a revision of the prior ILAE 1981 classification of seizures. Distinguishing between seizure types is important since different types of seizures may have different causes, outcomes, and treatments.
Causes of seizuresGenerally, seizures are observed in patients who do not have epilepsy. There are many causes of seizures. Organ failure, medication and medication withdrawal, cancer, imbalance of electrolytes, hypertensive encephalopathy, may be some of its potential causes. The factors that lead to a seizure are often complex and it may not be possible to determine what causes a particular seizure, what causes it to happen at a particular time, or how often seizures occur. Malnutrition and overnutrition may increase the risk of seizures.
Psychogenic non-epileptic seizurePsychogenic non-epileptic seizures (PNES), which have been more recently classified as functional seizures, are events resembling an epileptic seizure, but without the characteristic electrical discharges associated with epilepsy. PNES fall under the category of disorders known as functional neurological disorders (FND), also known as conversion disorders. These are typically treated by psychologists or psychiatrists. PNES has previously been called pseudoseizures, psychogenic seizures, and hysterical seizures, but these terms have fallen out of favor.
Linear separabilityIn Euclidean geometry, linear separability is a property of two sets of points. This is most easily visualized in two dimensions (the Euclidean plane) by thinking of one set of points as being colored blue and the other set of points as being colored red. These two sets are linearly separable if there exists at least one line in the plane with all of the blue points on one side of the line and all the red points on the other side. This idea immediately generalizes to higher-dimensional Euclidean spaces if the line is replaced by a hyperplane.
Non-epileptic seizureNon-epileptic seizures (NES), also known as non-epileptic events, are paroxysmal events that appear similar to an epileptic seizure but do not involve abnormal, rhythmic discharges of neurons in the brain. Symptoms may include shaking, loss of consciousness, and loss of bladder control. They may or may not be caused by either physiological or psychological conditions. Physiological causes include fainting, sleep disorders, and heart arrhythmias. Psychological causes are known as psychogenic non-epileptic seizures.
Reflex seizureReflex seizures are epileptic seizures that are consistently induced by a specific stimulus or trigger making them distinct from other epileptic seizures, which are usually unprovoked. Reflex seizures are otherwise similar to unprovoked seizures and may be focal (simple or complex), generalized, myoclonic, or absence seizures. Epilepsy syndromes characterized by repeated reflex seizures are known as reflex epilepsies. Photosensitive seizures are often myoclonic, absence, or focal seizures in the occipital lobe, while musicogenic seizures are associated with focal seizures in the temporal lobe.
Epileptic spasmsEpileptic spasms is an uncommon-to-rare epileptic disorder in infants, children and adults. One of the other names of the disorder, West syndrome, is in memory of the English physician, William James West (1793–1848), who first described it in an article published in The Lancet in 1841. The original case actually described his own son, James Edwin West (1840–1860). Other names for it are "generalized flexion epilepsy", "infantile epileptic encephalopathy", "infantile myoclonic encephalopathy", "jackknife convulsions", "massive myoclonia" and "Salaam spasms".
Focal seizureFocal seizures (also called partial seizures and localized seizures) are seizures which affect initially only one hemisphere of the brain. The brain is divided into two hemispheres, each consisting of four lobes – the frontal, temporal, parietal and occipital lobes. A focal seizure is generated in and affects just one part of the brain – a whole hemisphere or part of a lobe. Symptoms will vary according to where the seizure occurs. When seizures occur in the frontal lobe the patient may experience a wave-like sensation in the head.
Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
Feature engineeringFeature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data. Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific- based feature engineering has become obsolete for vision and speech processing.
