Instance Selection and Construction for Data Mining

1. Front Matter

   Pages i-xxv

   PDF

2. Background and Foundation

   1. Front Matter

      Pages 1-1

      PDF

   2. Data Reduction via Instance Selection

      • Huan Liu, Hiroshi Motoda

      Pages 3-20

   3. Sampling: Knowing Whole from Its Part

      • Baohua Gu, Feifang Hu, Huan Liu

      Pages 21-38

   4. A Unifying View on Instance Selection

      • Thomas Reinartz

      Pages 39-56

3. Instance Selection Methods

   1. Front Matter

      Pages 57-57

      PDF

   2. Competence Guided Instance Selection for Case-Based Reasoning

      • Barry Smyth, Elizabeth McKenna

      Pages 59-76

   3. Identifying Competence-Critical Instances for Instance-Based Learners

      • Henry Brighton, Chris Mellish

      Pages 77-94

   4. Genetic-Algorithm-Based Instance and Feature Selection

      • Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii

      Pages 95-112

   5. The Landmark Model: An Instance Selection Method for Time Series Data

      • Chang-Shing Perng, Sylvia R. Zhang, D. Stott Parker

      Pages 113-130

4. Use of Sampling Methods

   1. Front Matter

      Pages 131-131

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   2. Adaptive Sampling Methods for Scaling up Knowledge Discovery Algorithms

      • Carlos Domingo, Ricard Gavaldà, Osamu Watanabe

      Pages 133-150

   3. Progressive Sampling

      • Foster Provost, David Jensen, Tim Oates

      Pages 151-170

   4. Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes

      • Jean-Hugues Chauchat, Ricco Rakotomalala

      Pages 171-188

   5. Incremental Classification Using Tree-Based Sampling for Large Data

      • Hankil Yoon, Khaled Alsabti, Sanjay Ranka

      Pages 189-206

5. Unconventional Methods

   1. Front Matter

      Pages 207-207

      PDF

   2. Instance Construction via Likelihood-Based Data Squashing

      • David Madigan, Nandini Raghavan, William DuMouchel, Martha Nason, Christian Posse, Greg Ridgeway

      Pages 209-226

   3. Learning via Prototype Generation and Filtering

      • Wai Lam, Chi-Kin Keung, Charles X. Ling

      Pages 227-244

   4. Instance Selection Based on Hypertuples

      • Hui Wang

      Pages 245-262

   5. KBIS: Using Domain Knowledge to Guide Instance Selection

      • Peggy Wright, Julia Hodges

      Pages 263-279

The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency.
One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc.
Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.

• DOM
• Time series
• algorithms
• case-based reasoning
• classification
• data mining
• database
• filtering
• genetic algorithm
• genetic algorithms
• knowledge
• knowledge discovery
• learning

• Arizona State University, USA

  Huan Liu

• Osaka University, Japan

  Hiroshi Motoda

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