... Supervised learners, i.e., algorithms for classification and regression, are termed “classifiers” by
Weka. (Numeric prediction, i.e., regression, is interpreted as prediction of a continuous class.)
R interface functions to Weka classifiers are created by make_Weka_classifier, and have formals
... the associated class label is unknown, the attribute values of the tuple are tested
against the decision tree. A path is traced from the root to a leaf node, which holds
the class prediction for that tuple. Decision trees can easily be converted to classification rules.
“Why are decision tree classi ...
Ensemble of Feature Selection Techniques for High
... classified into three categories: filters, wrappers and hybrids . In this thesis we will be
using four filter based feature ranking techniques and one wrapper based feature ranking
technique.Classification is a data mining technique used to classify or predict group
membership for data instances. ...
Combining Classifiers with Meta Decision Trees
... obtain the final prediction from the predictions of the base-level classifiers. Cascading is an
iterative process of combining classifiers: at each iteration, the training data set is extended
with the predictions obtained in the previous iteration.
The work presented here focuses on combining the p ...
The 2009 Knowledge Discovery in Data Competition (KDD Cup
... setting, which is typical of large-scale industrial applications. A large database was made available by the French Telecom company, Orange with tens of thousands of examples and variables.
This dataset is unusual in that it has a large number of variables making the problem particularly
A Comparative Study of Discretization Methods for Naive
... to avoid the fragmentation problem . If an attribute has many values, a split
on this attribute will result in many branches, each of which receives relatively
few training instances, making it difficult to select appropriate subsequent tests.
Naive-Bayes learning considers attributes independen ...
Chapter 6 A SURVEY OF TEXT CLASSIFICATION
... is used to predict a class label for this instance. In the hard version
of the classiﬁcation problem, a particular label is explicitly assigned to
the instance, whereas in the soft version of the classiﬁcation problem,
a probability value is assigned to the test instance. Other variations of
the cla ...
SAWTOOTH: Learning on huge amounts of data
... achieve the best possible classification accuracy at any given point in time.
Even though many algorithms have been offered to handle concept drift, the
scalability problem still remains open for such algorithms.
In this thesis we propose a scalable algorithm for data classification
from very large ...
CENG 464 Introduction to Data Mining
... values (class labels) in a classifying attribute and uses it in classifying
• Numeric Prediction
– models continuous-valued functions, i.e., predicts unknown or missing
• Typical applications
– Credit/loan approval
– Medical diagnosis: if a tumor is cancerous or benign
– Fraud detect ...
Classification in the Presence of Background Domain Knowledge
... using existing domain knowledge to enrich and better focus the results on user expectations remains
open to further developments [Cao, 2010; Domingos, 2003; Yang and Wu, 2006].
While it is true that significant work has been done in some areas, namely pattern mining, to inject
knowledge about the do ...
... naive Bayes Classifier:
P(Refund=Yes|No) = 4/9
P(Refund=No|No) = 5/9
P(Refund=Yes|Yes) = 1/5
P(Refund=No|Yes) = 4/5
P(Marital Status=Single|No) = 3/10
P(Marital Status=Married|No) = 5/10
P(Marital Status=Single|Yes) = 3/6
On Combined Classifiers, Rule Induction and Rough Sets
... classification, reducing the set of attributes or generating decision rules from
data. It is also said that the aim is to synthesize reduced, approximate models of
concepts from data . The transparency and explainability of such models to
human is an important property. Up to now rough sets bas ...
Statistical Learning Theory
... which indicates whether this compound is useful for drug design or not. It is expensive to
find out whether a chemical compound possesses certain properties that render it a suitable
drug because this would require running extensive lab experiments. As a result, only rather
few compounds Xi have kno ...
7class - Laurentian University | Program Detail
... Let X be a data sample (“evidence”): class label is unknown
Let H be a hypothesis that X belongs to class C
Classification is to determine P(H|X), (posteriori
probability), the probability that the hypothesis holds given
the observed data sample X
P(H) (prior probability), the initial probability
... • Informally, this can be viewed as
posteriori = likelihood x prior/evidence
• Predicts X belongs to Ci iff the probability P(Ci|X) is the highest
among all the P(Ck|X) for all the k classes
• Practical difficulty: It requires initial knowledge of many
probabilities, involving significant computatio ...
Naive Bayes classifier
In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s, and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate preprocessing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis.Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.In the statistics and computer science literature, Naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method; Russell and Norvig note that ""[naive Bayes] is sometimes called a Bayesian classifier, a somewhat careless usage that has prompted true Bayesians to call it the idiot Bayes model.""