| Preface |
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viii | |
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Chapter 1 Biological Background |
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1 | (5) |
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1 | (3) |
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4 | (1) |
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5 | (1) |
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Chapter 2 Gene Expression Data Sets |
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6 | (4) |
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Biological Data and Their Characteristics |
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6 | (3) |
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9 | (1) |
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9 | (1) |
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Chapter 3 Introduction to Data Classification |
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10 | (3) |
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Problem of Data Classification |
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10 | (2) |
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12 | (1) |
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12 | (1) |
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13 | (19) |
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13 | (15) |
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28 | (2) |
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30 | (2) |
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Chapter 5 Nearest Neighbor |
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32 | (21) |
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Nearest Neighbor Classification |
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32 | (16) |
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48 | (3) |
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51 | (2) |
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Chapter 6 Classification Tree |
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53 | (15) |
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53 | (13) |
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66 | (1) |
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67 | (1) |
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Chapter 7 Support Vector Machines |
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68 | (49) |
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68 | (44) |
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112 | (2) |
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114 | (3) |
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Chapter 8 Introduction to Feature and Gene Selection |
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117 | (6) |
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Problem of Feature Selection |
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117 | (4) |
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121 | (1) |
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121 | (2) |
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Chapter 9 Feature Selection Based on Elements of Game Theory |
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123 | (18) |
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Feature Selection Based on the Shapley Value |
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123 | (16) |
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139 | (1) |
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139 | (2) |
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Chapter 10 Kernel-Based Feature Selection with the Hilbert-Schmidt Independence Criterion |
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141 | (18) |
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Kernel Methods and Feature Selection |
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141 | (14) |
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155 | (2) |
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157 | (2) |
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Chapter 11 Extreme Value Distribution Based Gene Selection |
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159 | (18) |
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Blend of Elements of Extreme Value Theory and Logistic Regression |
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159 | (15) |
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174 | (1) |
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175 | (2) |
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Chapter 12 Evolutionary Algorithm for Identifying Predictive Genes |
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177 | (26) |
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Evolutionary Search for Optimal or Near-Optimal Set of Genes |
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177 | (22) |
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199 | (2) |
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201 | (2) |
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Chapter 13 Redundancy-Based Feature Selection |
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203 | (20) |
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203 | (17) |
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220 | (2) |
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222 | (1) |
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Chapter 14 Unsupervised Feature Selection |
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223 | (13) |
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Unsupervised Feature Filtering |
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223 | (11) |
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234 | (2) |
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Chapter 15 Differential Evolution for Finding Predictive Gene Subsets |
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236 | (16) |
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Differential Evolution: Global, Evolution Strategy Based Optimization Method |
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236 | (13) |
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249 | (2) |
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251 | (1) |
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Chapter 16 Ensembles of Classifiers |
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252 | (8) |
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252 | (5) |
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257 | (1) |
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258 | (2) |
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Chapter 17 Classifier Ensembles Built on Subsets of Features |
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260 | (36) |
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Shaking Stable Classifiers |
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260 | (31) |
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291 | (3) |
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294 | (2) |
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Chapter 18 Bagging and Random Forests |
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296 | (18) |
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Bootstrap and its Use in Classifier Ensembles |
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296 | (15) |
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311 | (2) |
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313 | (1) |
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Chapter 19 Boosting and AdaBoost |
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314 | (15) |
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Weighted Learning, Boosting and AdaBoost |
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314 | (12) |
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326 | (2) |
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328 | (1) |
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Chapter 20 Ensemble Gene Selection |
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329 | (5) |
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Getting Important Genes out of a Pool |
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329 | (4) |
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333 | (1) |
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333 | (1) |
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Chapter 21 Introduction to Classification Error Estimation |
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334 | (7) |
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Problem of Classification Error Estimation |
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334 | (4) |
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338 | (2) |
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340 | (1) |
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Chapter 22 ROC Curve, Area under it, Other Classification Performance Characteristics and Statistical Tests |
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341 | (42) |
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Classification Performance Evaluation |
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341 | (38) |
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379 | (2) |
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381 | (2) |
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Chapter 23 Bolstered Resubstitution Error |
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383 | (23) |
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Alternative to Traditional Error Estimators |
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383 | (21) |
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404 | (1) |
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405 | (1) |
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Chapter 24 Performance Evaluation: Final Check |
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406 | (8) |
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Bayesian Confidence (Credible) Interval |
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406 | (6) |
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412 | (1) |
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413 | (1) |
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Chapter 25 Application Examples |
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414 | (22) |
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Joining All Pieces Together |
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414 | (20) |
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434 | (1) |
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435 | (1) |
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436 | (3) |
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436 | (1) |
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437 | (2) |
| About the Author |
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439 | (1) |
| Index |
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440 | |
