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Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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Classification of Systems-I01:26

Classification of Systems-I

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Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Videos

Supervised and traditional term weighting methods for automatic text categorization.

Man Lan1, Chew Lim Tan, Jian Su

  • 1Department of Computer Science and Technology, East China Normal University, Shanghai, China. mlan@cs.ecnu.edu.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 21, 2009
PubMed
Summary
This summary is machine-generated.

A new supervised term weighting method, tf.rf, consistently improves text categorization performance. This approach outperforms traditional and other supervised methods, offering better term discrimination for vector space models.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Information Retrieval
  • Machine Learning

Background:

  • Text representation in vector space models (VSM) is crucial for computer-based document recognition and classification.
  • Term weighting methods are essential for assigning importance to terms, thereby enhancing text categorization accuracy.
  • Existing unsupervised and supervised term weighting methods show varied performance in VSM applications.

Purpose of the Study:

  • To investigate the performance of various unsupervised and supervised term weighting methods for text categorization.
  • To propose a novel supervised term weighting method, tf.rf, designed to enhance term discriminating power.
  • To evaluate the effectiveness of tf.rf against established methods using benchmark datasets and machine learning algorithms.

Main Methods:

  • Evaluation of traditional unsupervised and several supervised term weighting techniques.
  • Implementation and testing of Support Vector Machines (SVM) and kappa Nearest Neighbors (kappa NN) classifiers.
  • Development and application of the proposed tf.rf supervised term weighting method, considering relevant document distribution.

Main Results:

  • Supervised term weighting methods demonstrated mixed performance across experiments.
  • The proposed tf.rf method consistently outperformed other term weighting approaches.
  • Information theory and statistical metric-based supervised methods yielded the poorest results, while tf.idf showed inconsistent performance across datasets.

Conclusions:

  • The tf.rf supervised term weighting method offers a significant improvement in text categorization within VSM.
  • The effectiveness of supervised term weighting methods is highly dependent on the specific approach and dataset.
  • Further research into advanced supervised term weighting strategies is warranted for optimizing text classification performance.