[PDF] Top 20 Comparative Study between Various Classification Algorithms for Classification of Cardiotocogram Data

... communication between staff, or delay in taking appropriate ...other data mining techniques can be used to analyze and classify the CTG data to avoid human mistakes and to assist doctors to take a ... See full document

... weigh various factors including demographic as well as others to make final offer to an ...internal data for predicting salary of a new hire. But such data is not available for external usage and ... See full document

... Abstract: Data Mining is the technique of finding new information from the existing data on the basis of patterns that has been shown in the data to predict some conclusion from the ...data. ... See full document

... Although FCM has advantages in clustering, such as the fast convergence and degree of memberships which makes the clustering more realistic rather than crisp clustering, it also suffers from various limitations. ... See full document

... for data compression is the "compression ratio", or ratio of the size of a compressed file to the original uncompressed ...a data file takes up 50 kilobytes (KB). Using data compression ... See full document

... the data clustering technique, in this technique a dataset is grouped into more than one cluster, with every data point in the dataset belonging to a ...a data point can be classified into one or ... See full document

... the classification of an audio ...for classification into various music ...of data, feature selection using gain ratio and finally a classification stage using splitting ...successful ... See full document

... training data using a hyper plane. For example, the training data for face detection consists of group of images that are faces and another group of images that are not faces (in other words all other ... See full document

... - Classification is a technique in which the data is categorized into 2 or more ...non-linear data. The main goal of the classification problem is to identify the category of the test ...for ... See full document

... Neural networks usually have long training times and are therefore more suitable for applications where this is feasible. They require a number of parameters that are typically best determined empirically such as the ... See full document

... and various social networks ...text classification with machine learning and works with collections of humans’ opinions or customer feedback data expressed by short text ... See full document

... J4.8 decision trees algorithm is an open source Java implementation of the C4.5. It grows a tree and uses divide-and-conquer algorithm. It is a predictive machine-learning model that decides the target value (dependent ... See full document

... large data sets to extract and discover previously unknown structures and relations out of such huge heaps of ...of data stored in files, databases, and other repositories, it is increasingly important, if ... See full document

... in data extraction techniques. They present study to detect malware software by using data mining ...comparison between these classification algorithms to choosing the best ... See full document

... training data set S=S 1 ,S 2 ...the data that most efficiently splits its set of samples into subsets such that it results in one class or the ...difference between two probability distributions P ... See full document

... Labatut, Vincent, and Hocine Cherifi. [2] the authors in this work, reviewed the main measures used to assess accuracy from different classification. They consider the case where a person wants to compare ... See full document

... for classification and prediction, extraction and summarization of sentiments and emotions expressed by various peoples in online text ...perform various detection tasks at different text-granularity ... See full document

... Bayesian network is a directed, acyclic graph, which represents the set of random variables and their dependencies. The Bayesian classifier is constructed based on the bayesian network [17]. Naive Bayes classifier is a ... See full document

... of data containing observations (or instances) whose category membership is ...[1] classification is considered an instance of supervised learning, ...grouping data into categories based on some ... See full document

... This means that in order to find in which class we should classify a new document, we must estimate the product of the probability of each word of the document given a particular class (likelihood), multiplied by the ... See full document