A comparison between statistical and Data Mining methods for credit
... The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed approaches: Regression and Data Mining techniques given our limited sample size. We are aware that our conclusions are based on a very limited database. We would appreciate if others coul ...
... The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed approaches: Regression and Data Mining techniques given our limited sample size. We are aware that our conclusions are based on a very limited database. We would appreciate if others coul ...
Soc709 Lab 11
... Hetereoskedasticity is a problem because the variance of the error term in not the same for each case. As a result, the standard formula for the variance of the coefficients in no longer valid, and estimates of the standard errors will be biased. Note that the point estimates of the coefficients are ...
... Hetereoskedasticity is a problem because the variance of the error term in not the same for each case. As a result, the standard formula for the variance of the coefficients in no longer valid, and estimates of the standard errors will be biased. Note that the point estimates of the coefficients are ...
An Overview of Data Mining Techniques Applied for Heart Disease
... which indicates the minimum instances that a leaf should have. C means the confidence threshold which is considered for pruning. By changing these two factors, the accuracy of algorithm can be increased and the error can be decreased [9]. 2) RIPPER classification algorithm RIPPER stands for Repeated ...
... which indicates the minimum instances that a leaf should have. C means the confidence threshold which is considered for pruning. By changing these two factors, the accuracy of algorithm can be increased and the error can be decreased [9]. 2) RIPPER classification algorithm RIPPER stands for Repeated ...
Paper
... a level-wise search, in which n-item sets are used to explore (n+1)-item sets. The A-priori algorithm is designed to reduce the number of pairs that must be counted, at the expense of performing two passes over data, rather than one pass. A-Priori Algorithm--- Pass 1 First, we create two tables in w ...
... a level-wise search, in which n-item sets are used to explore (n+1)-item sets. The A-priori algorithm is designed to reduce the number of pairs that must be counted, at the expense of performing two passes over data, rather than one pass. A-Priori Algorithm--- Pass 1 First, we create two tables in w ...
An Influential Algorithm for Outlier Detection
... approach method involve the investigation not only local density but also studied local density of its nearest neighbors [5]. This method identify the outlier by checking the main features or characteristics of object in database the object that are deviate from these feature are consider as outlier ...
... approach method involve the investigation not only local density but also studied local density of its nearest neighbors [5]. This method identify the outlier by checking the main features or characteristics of object in database the object that are deviate from these feature are consider as outlier ...
Machine Learning in Time Series Databases (and Outline of Tutorial I
... The Generic Data Mining Algorithm (revisited) • Create an approximation of the data, which will fit in main memory, yet retains the essential features of interest • Approximately solve the problem at hand in main memory • Make (hopefully very few) accesses to the original data on disk to confirm the ...
... The Generic Data Mining Algorithm (revisited) • Create an approximation of the data, which will fit in main memory, yet retains the essential features of interest • Approximately solve the problem at hand in main memory • Make (hopefully very few) accesses to the original data on disk to confirm the ...
Clustering based Two-Stage Text Classification Requiring Minimal
... classification, including feature compression or extraction [10], semisupervised learning [11], and clustering in large-scale classification problems [12,13]. The following will review several related work about clustering aiding classification in the area of semi-supervised learning. A comprehensiv ...
... classification, including feature compression or extraction [10], semisupervised learning [11], and clustering in large-scale classification problems [12,13]. The following will review several related work about clustering aiding classification in the area of semi-supervised learning. A comprehensiv ...
Document
... • Clusters can be visualized and compared to “true” clusters (if given) • Evaluation based on loglikelihood if clustering scheme produces a probability distribution ...
... • Clusters can be visualized and compared to “true” clusters (if given) • Evaluation based on loglikelihood if clustering scheme produces a probability distribution ...
CUCIS - Northwestern University
... to evaluate different machine learning algorithms [22]. A total of 20 classification schemes were used. 5 basic classifiers, and a combination of the 3 meta classifiers with the 5 basic classifiers as an underlying classifier. We also performed ensemble voting of 3 of the performing classification s ...
... to evaluate different machine learning algorithms [22]. A total of 20 classification schemes were used. 5 basic classifiers, and a combination of the 3 meta classifiers with the 5 basic classifiers as an underlying classifier. We also performed ensemble voting of 3 of the performing classification s ...
Mining mass-spectra for diagnosis and biomarker - (CUI)
... early stroke diagnosis using SELDI mass-spectrometry coupled with analysis tools from machine learning and data-mining. Data consist of 42 specimen samples, ie, mass-spectra divided to two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that corr ...
... early stroke diagnosis using SELDI mass-spectrometry coupled with analysis tools from machine learning and data-mining. Data consist of 42 specimen samples, ie, mass-spectra divided to two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that corr ...
Classification and Prediction
... attributes (variables) are often correlated. Attempts to overcome this limitation: Bayesian networks, that combine Bayesian reasoning with causal relationships between attributes Decision trees, that reason on one attribute at the time, considering most important attributes first ...
... attributes (variables) are often correlated. Attempts to overcome this limitation: Bayesian networks, that combine Bayesian reasoning with causal relationships between attributes Decision trees, that reason on one attribute at the time, considering most important attributes first ...
Very non-resistant
... http://www.stat.sc.edu/~west/applets/box.html • Mean feels outliers much more strongly • Leaves “range of most of data” • Good notion of “center”? (perhaps not) • Median affected very minimally ...
... http://www.stat.sc.edu/~west/applets/box.html • Mean feels outliers much more strongly • Leaves “range of most of data” • Good notion of “center”? (perhaps not) • Median affected very minimally ...
Classification: basic concepts
... Classification is to determine P(H|X), (i.e., posteriori probability): the probability that the hypothesis holds given the observed data sample X P(H) (prior probability): the initial probability E.g., X will buy computer, regardless of age, income, … P(X): probability that sample data is observed ...
... Classification is to determine P(H|X), (i.e., posteriori probability): the probability that the hypothesis holds given the observed data sample X P(H) (prior probability): the initial probability E.g., X will buy computer, regardless of age, income, … P(X): probability that sample data is observed ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.