Ebook: Instance Selection and Construction for Data Mining

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.

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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.

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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.
Content:
Front Matter....Pages i-xxv
Front Matter....Pages 1-1
Data Reduction via Instance Selection....Pages 3-20
Sampling: Knowing Whole from Its Part....Pages 21-38
A Unifying View on Instance Selection....Pages 39-56
Front Matter....Pages 57-57
Competence Guided Instance Selection for Case-Based Reasoning....Pages 59-76
Identifying Competence-Critical Instances for Instance-Based Learners....Pages 77-94
Genetic-Algorithm-Based Instance and Feature Selection....Pages 95-112
The Landmark Model: An Instance Selection Method for Time Series Data....Pages 113-130
Front Matter....Pages 131-131
Adaptive Sampling Methods for Scaling up Knowledge Discovery Algorithms....Pages 133-150
Progressive Sampling....Pages 151-170
Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes....Pages 171-188
Incremental Classification Using Tree-Based Sampling for Large Data....Pages 189-206
Front Matter....Pages 207-207
Instance Construction via Likelihood-Based Data Squashing....Pages 209-226
Learning via Prototype Generation and Filtering....Pages 227-244
Instance Selection Based on Hypertuples....Pages 245-262
KBIS: Using Domain Knowledge to Guide Instance Selection....Pages 263-279
Front Matter....Pages 281-281
Instance Sampling for Boosted and Standalone Nearest Neighbor Classifiers....Pages 283-300
Prototype Selection Using Boosted Nearest-Neighbors....Pages 301-318
DAGGER: Instance Selection for Combining Multiple Models Learnt from Disjoint Subsets....Pages 319-336
Front Matter....Pages 337-337
Using Genetic Algorithms for Training Data Selection in RBF Networks....Pages 339-356
An Active Learning Formulation for Instance Selection with Applications to Object Detection....Pages 357-374
Front Matter....Pages 337-337
Filtering Noisy Instances and Outliers....Pages 375-394
Instance Selection Based on Support Vector Machine for Knowledge Discovery in Medical Database....Pages 395-409
Back Matter....Pages 410-416

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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.
Content:
Front Matter....Pages i-xxv
Front Matter....Pages 1-1
Data Reduction via Instance Selection....Pages 3-20
Sampling: Knowing Whole from Its Part....Pages 21-38
A Unifying View on Instance Selection....Pages 39-56
Front Matter....Pages 57-57
Competence Guided Instance Selection for Case-Based Reasoning....Pages 59-76
Identifying Competence-Critical Instances for Instance-Based Learners....Pages 77-94
Genetic-Algorithm-Based Instance and Feature Selection....Pages 95-112
The Landmark Model: An Instance Selection Method for Time Series Data....Pages 113-130
Front Matter....Pages 131-131
Adaptive Sampling Methods for Scaling up Knowledge Discovery Algorithms....Pages 133-150
Progressive Sampling....Pages 151-170
Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes....Pages 171-188
Incremental Classification Using Tree-Based Sampling for Large Data....Pages 189-206
Front Matter....Pages 207-207
Instance Construction via Likelihood-Based Data Squashing....Pages 209-226
Learning via Prototype Generation and Filtering....Pages 227-244
Instance Selection Based on Hypertuples....Pages 245-262
KBIS: Using Domain Knowledge to Guide Instance Selection....Pages 263-279
Front Matter....Pages 281-281
Instance Sampling for Boosted and Standalone Nearest Neighbor Classifiers....Pages 283-300
Prototype Selection Using Boosted Nearest-Neighbors....Pages 301-318
DAGGER: Instance Selection for Combining Multiple Models Learnt from Disjoint Subsets....Pages 319-336
Front Matter....Pages 337-337
Using Genetic Algorithms for Training Data Selection in RBF Networks....Pages 339-356
An Active Learning Formulation for Instance Selection with Applications to Object Detection....Pages 357-374
Front Matter....Pages 337-337
Filtering Noisy Instances and Outliers....Pages 375-394
Instance Selection Based on Support Vector Machine for Knowledge Discovery in Medical Database....Pages 395-409
Back Matter....Pages 410-416
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