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About 63.2% of the original data end up in the bootstrap, and the remaining 36.8% form the test set (since (1 1/d)d e-1 = 0.368). These slides are based on Tom Mitchells book Machine Learning Lazy learning vs. eager learning Processing is delayed until a new instance must be classified. ht _rels/.rels ( J1!}7*"loD c2Haa-?$Yon ^AX+xn 278O Instances represented as points in a Euclidean space. author = "Paola Galdi and Roberto Tagliaferri". }, 5 Classifier Evaluation Metrics: ExampleActual Class\Predicted class cancer = yes cancer = no Total Recognition(%) 90 210 300 30.00 (sensitivity 140 9560 9700 98.56 (specificity) 230 9770 10000 96.40 (accuracy) Precision = 90/230 = 39.13% Recall = 90/300 = 30.00% 12 {

", To use this website, you must agree to our. The complexity of SVM training is characterized by the # of support vectors rather than the dimensionality of the data.

"@context": "http://schema.org", ", Research output: Chapter in Book/Report/Conference proceeding Chapter. "contentUrl": "https://slideplayer.com/slide/4893678/16/images/7/Other+classifiers+Fuzzy+Set+Approach%3A.jpg",

=U*8-#0A" Q2yJsaF8==,JRad]6pQ ~WDk0 # h`w {h"f0A ht9q>KlnV}XA`dFp#M)*6Vf,F`4nc&1 #l5Gq{E. "@type": "ImageObject", Fuzzy Set Approach: Attribute values are converted to fuzzy values along with degree of membership. P. FP. Stratified k-fold cross-validation is recommended for accuracy estimation. "width": "800" ShT^?0)Lmc Classifier Evaluation Metrics: Confusion MatrixActual class\Predicted class C1 C1 True Positives (TP) False Negatives (FN) False Positives (FP) True Negatives (TN) Example of Confusion Matrix: Actual class\Predicted class buy_computer = yes buy_computer = no Total buy_computer = yes 6954 46 7000 412 2588 3000 7366 2634 10000 Given m classes, an entry, CMi,j in a confusion matrix indicates # of tuples in class i that were labeled by the classifier as class j May have extra rows/columns to provide totals 9 "width": "800" { To view this video please enable JavaScript, and consider upgrading to a web browser that A model with perfect accuracy will have an area of", Associative classification has been found to be often more accurate than some traditional classification methods, such as C", in each fold is approx.

Selection of Classifier: BPNN vs Support Vector Machine (SVM)",

"contentUrl": "https://slideplayer.com/slide/4893678/16/images/8/Model+Evaluation+and+Selection.jpg", S5=G%wn;cz#4|h|vCA@l0 PK !

[Content_Types].xml ( n0CHn>N(TN`)Y4s^%, tH7u1iLiT^E4#cE$L|=bv`"g-M/1jaAB5n,/ K)iAm}(#rGw[:Lat6 T(*3a]ZG8,yU {oKkv"Z[0.]iO(rj$;?c0r_Fzznb8)5:%:t+tt_).R|aHk#G_:2|ud#WG_:r|u#WG_9:r|uxmQXo] cts3QUn+8{_c]W7w$5{j\0e)L]c Other classification methods: lazy learners (KNN), genetic algorithms, rough set and fuzzy set approaches. BPNN tends to suffer from over-fitting problems and doesnt lead to unique solution owing to differences in their initial weight.

Lazy method effectively uses a richer hypothesis space since it uses many local linear functions to form an implicit global approximation to the target function. "@context": "http://schema.org", Issues such as accuracy, training time, robustness, scalability, and interpretability must be considered and can involve trade-offs.

Offspring are generated by crossover and mutation Rough Sets: a given class C is approximated by two sets: a lower approximation (certain to be in C) and an upper approximation (cannot be described as not belonging to C) . Data Mining Classification: Alternative Techniques. "@context": "http://schema.org", The closer to the diagonal line (i.e., the closer the area is to 0.5), the less accurate is the model. True Positives (TP) False Negatives (FN) False Positives (FP) True Negatives (TN) Example of Confusion Matrix: Actual class\\Predicted class. "name": "Other classifiers Fuzzy Set Approach:", By continuing you agree to the use of cookies, University of Edinburgh Research Explorer data protection policy. Use validation test set of class-labeled tuples instead of training set when assessing accuracy. ", { Model Selection: ROC CurvesROC (Receiver Operating Characteristics) curves: for visual comparison of classification models Originated from signal detection theory Shows the trade-off between the true positive rate and the false positive rate The area under the ROC curve is a measure of the accuracy of the model Rank the test tuples in decreasing order: the one that is most likely to belong to the positive class appears at the top of the list The closer to the diagonal line (i.e., the closer the area is to 0.5), the less accurate is the model Vertical axis represents the true positive rate Horizontal axis rep. the false positive rate The plot also shows a diagonal line A model with perfect accuracy will have an area of 1.0 15 About 63.2% of the original data end up in the bootstrap, and the remaining 36.8% form the test set (since (1 1/d)d e-1 = 0.368) Repeat the sampling procedure k times, overall accuracy of the model: 14 ", BPNN will have poor generalization when the number of class samples is limited. assigns \u00df times as much weight to recall as to precision. "@context": "http://schema.org", title = "Data Mining: Accuracy and Error Measures for Classification and Prediction". *Stratified cross-validation*: folds are stratified so that class dist.

"width": "800" @inbook{655813e44ead4615bc5668ac9224d205. Specificity: True Negative recognition rate.

}, 8 TN. Sh" ppt/slides/_rels/slide9.xml.relsAK0!ldm'!=x8o|_>)Kl (b}dSdJa07c">K`3dvMsoB>/st\0.

~c +S,8zf^ja;&92_0nlfCa1baF "name": "Classifier Evaluation Metrics: Precision and Recall, and F-measures",

4. The area under the ROC curve is a measure of the accuracy of the model. Mine data to find strong associations between frequent patterns (conjunctions of attribute-value pairs) and class labels. the same as that in the initial data. ", "width": "800" "@type": "ImageObject", "width": "800" "name": "Comparison of BPNN and SVM", Other metrics to consider? Association rules are generated in the form of.

Repeat the sampling procedure k times, overall accuracy of the model: ROC (Receiver Operating Characteristics) curves: for visual comparison of classification models, Shows the trade-off between the true positive rate and the false positive rate, The area under the ROC curve is a measure of the accuracy of the model, Rank the test tuples in decreasing order: the one that is most likely to belong to the positive class appears at the top of the list, The closer to the diagonal line (i.e., the closer the area is to 0.5), the less accurate is the model, Vertical axis represents the true positive rate, Horizontal axis rep. the false positive rate, A model with perfect accuracy will have an area of 1.0, Effective and advanced classification methods, Selection of Classifier: BPNN vs Support Vector Machine (SVM), comparisons of the different classification methods: No single method has been found to be superior over all others for all data sets. The fitness of a rule is represented by its classification accuracy on a set of training examples. The complexity of SVM training is characterized by the # of support vectors rather than the dimensionality of the data. Thus, an SVM with a small number of support vectors can have good generalization, even when the dimensionality of the data is high.

doi = "10.1016/B978-0-12-809633-8.20474-3". "width": "800" Classifier Accuracy, or recognition rate: percentage of test set tuples that are correctly classified. a\^hD.Cy1BYz F measure (F1 or F-score): harmonic mean of precision and recall, F: weighted measure of precision and recall, assigns times as much weight to recall as to precision, Precision = 90/230 = 39.13% Recall = 90/300 = 30.00%, Given data is randomly partitioned into two independent sets, Training set (e.g., 2/3) for model construction, Test set (e.g., 1/3) for accuracy estimation, Repeat holdout k times, accuracy = avg. "name": "Lazy vs. Stratified k-fold cross-validation is recommended for accuracy estimation. { "contentUrl": "https://slideplayer.com/slide/4893678/16/images/14/Evaluating+Classifier+Accuracy%3A+Bootstrap.jpg", \u00ac C1. TP. "name": "Model Selection: ROC Curves", The fitness of a rule is represented by its classification accuracy on a set of training examples.

Originated from signal detection theory. "description": "Holdout method. Associative ClassificationAssociative classification: Major steps Mine data to find strong associations between frequent patterns (conjunctions of attribute-value pairs) and class labels Association rules are generated in the form of P1 ^ p2 ^ pl Aclass = C (conf, sup) Organize the rules to form a rule-based classifier Why effective? ( axes stretch, remove attributes), Weight the contribution of each of the k neighbors according to their distance to the query Xq. "contentUrl": "https://slideplayer.com/slide/4893678/16/images/16/Summary+Effective+and+advanced+classification+methods.+Selection+of+Classifier%3A+BPNN+vs+Support+Vector+Machine+%28SVM%29.jpg", Leave-one-out: k folds where k = # of tuples, for small sized data. C1. Inverse relationship between precision & recall. Methods for estimating a classifiers accuracy: Given m classes, an entry, CMi,j in a confusion matrix indicates # of tuples in class i that were labeled by the classifier as class j, May have extra rows/columns to provide totals, One class may be rare, e.g. }, 15 Attribute values are converted to fuzzy values along with degree of membership. BPNN tends to suffer from over-fitting problems and doesnt lead to unique solution owing to differences in their initial weight. "description": "ROC (Receiver Operating Characteristics) curves: for visual comparison of classification models. "@type": "ImageObject", CMAR (Classification based on Multiple Association Rules: Li, Han, Pei, ICDM\u201901) Classification: Statistical analysis on multiple rules.

Powered by Pure, Scopus & Elsevier Fingerprint Engine 2022 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. The complexity of SVM training is characterized by the # of support vectors rather than the dimensionality of the data Thus, an SVM with a small number of support vectors can have good generalization, even when the dimensionality of the data is high. Perfect score is 1.0 Inverse relationship between precision & recall F measure (F1 or F-score): harmonic mean of precision and recall, F: weighted measure of precision and recall assigns times as much weight to recall as to precision 11

Other classification methods: lazy learners (KNN), genetic algorithms, rough set and fuzzy set approaches Evaluation metrics include: accuracy, sensitivity, specificity, precision, recall, F measure, and F measure. Total. "name": "Typical Associative Classification Methods", PK ! Methods for estimating a classifier\u2019s accuracy: Holdout method, random subsampling. Lazy Learner: Instance-Based MethodsTypical approaches k-nearest neighbor approach Instances represented as points in a Euclidean space. Bagging and boosting can be used to increase overall accuracy by learning and combining a series of individual models.

}, 7 k-nearest neighbor approach. 10. Horizontal axis rep. the false positive rate. QZ 13. e( ppt/slides/_rels/slide8.xml.rels1k0B^;[JS{Vsl,]i} :{t8~1V7| 8 1 [Content_Types].xml ( r0;wn =\e%(}eYo>%oyi!'~8HR9n|O!&;}jQ=-_>=,H{fV0vX%bA4]q! Lazy vs. Modified over 7 years ago, 1 Recall: completeness what % of positive tuples did the classifier label as positive? "name": "Evaluating Classifier Accuracy: Holdout & Cross-Validation Methods", Thus, an SVM with a small number of support vectors can have good generalization, even when the dimensionality of the data is high. Repeat holdout k times, accuracy = avg. Classifier Evaluation Metrics: Precision and Recall, and F-measuresPrecision: exactness what % of tuples that the classifier labeled as positive are actually positive Recall: completeness what % of positive tuples did the classifier label as positive?

Uses symbolic representations and knowledge-based inference. "description": "SVM learning involves quadratic programming and BPNN can easily be learned in incremental fashion. CS 8751 ML & KDDEvaluating Hypotheses1 Sample error, true error Confidence intervals for observed hypothesis error Estimators Binomial distribution, Normal. F measure (F1 or F-score): harmonic mean of precision and recall, F\u00df: weighted measure of precision and recall. "contentUrl": "https://slideplayer.com/slide/4893678/16/images/12/Classifier+Evaluation+Metrics%3A+Example.jpg", The data tuples that did not make it into the training set end up forming the test set. { BPNN tends to suffer from over-fitting problems and doesn\u2019t lead to unique solution owing to differences in their initial weight. Cross-validation. About 63.2% of the original data end up in the bootstrap, and the remaining 36.8% form the test set (since (1 \u2013 1\/d)d \u2248 e-1 = 0.368) Repeat the sampling procedure k times, overall accuracy of the model: 14. "@type": "ImageObject", of the accuracies obtained, Cross-validation (k-fold, where k = 10 is most popular), Randomly partition the data into k mutually exclusive subsets, each approximately equal size, At i-th iteration, use Di as test set and others as training set, Leave-one-out: k folds where k = # of tuples, for small sized data, *Stratified cross-validation*: folds are stratified so that class dist. Model Evaluation Metrics for Performance Evaluation. }, 12 "@type": "ImageObject", booktitle = "Encyclopedia of Bioinformatics and Computational Biology", University of Edinburgh Research Explorer Home, Data Mining: Accuracy and Error Measures for Classification and Prediction, Chapter in Book/Report/Conference proceeding, https://doi.org/10.1016/B978-0-12-809633-8.20474-3, https://linkinghub.elsevier.com/retrieve/pii/B9780128096338204743, Encyclopedia of Bioinformatics and Computational Biology. }, 11 / Galdi, Paola; Tagliaferri, Roberto. buy_computer = no. 8. FJ&jObi5(day!Um;UB60;4 C*5: N*d\-d]b%AK { the same as that in the initial data 13 FN. { "width": "800" Accuracy. of the accuracies obtained Cross-validation (k-fold, where k = 10 is most popular) Randomly partition the data into k mutually exclusive subsets, each approximately equal size At i-th iteration, use Di as test set and others as training set Leave-one-out: k folds where k = # of tuples, for small sized data *Stratified cross-validation*: folds are stratified so that class dist. "$lp.b/4yj PK !

{ fraud, or HIV-positive. CPAR (Classification based on Predictive Association Rules: Yin & Han, SDM\u201903) Generation of predictive rules (FOIL-like analysis) but allow covered rules to retain with reduced weight.

"@context": "http://schema.org",

"description": "Genetic Algorithm: Similar to biological evolution. i.e., each time a tuple is selected, it is equally likely to be selected again and re-added to the training set. if A1 and \u00acA2 then C2 can be encoded as 100. fy .! Thank you! "@context": "http://schema.org", }, 13 "@type": "ImageObject", N. P\u2019 N\u2019 All.

Cross-validation (k-fold, where k = 10 is most popular) Randomly partition the data into k mutually exclusive subsets, each approximately equal size. May have extra rows\/columns to provide totals. Training set (e.g., 2\/3) for model construction. "description": "Actual class\\Predicted class. 9. Each applicable rule contributes a vote for membership in the categories. Each applicable rule contributes a vote for membership in the categories. The nearest neighbor are defined in terms of Euclidean distance, dist(X1, X2), k-NN returns the most common value among the k training examples nearest to xq (test sample), Curse of dimensionality: distance between neighbors could be dominated by irrelevant attributes. Given data is randomly partitioned into two independent sets. Aprendizagem baseada em instncias (K vizinhos mais prximos). A data set with d tuples is sampled d times, with replacement, resulting in a training set of d samples. Recognition(%) (sensitivity (specificity) (accuracy) Precision = 90\/230 = 39.13% Recall = 90\/300 = 30.00% 12. Each applicable rule contributes a vote for membership in the categories. {

( axes stretch, remove attributes) Locally weighted regression. Uses symbolic representations and knowledge-based inference, Genetic Algorithm: Similar to biological evolution, Each rule is represented by a string of bits, if A1 and A2 then C2 can be encoded as 100. Share buttons are a little bit lower.

"name": "Classifier Evaluation Metrics: Example", "@context": "http://schema.org", Mine data to find strong associations between frequent patterns (conjunctions of attribute-value pairs) and class labels, Association rules are generated in the form of, P1 ^ p2 ^ pl Aclass = C (conf, sup), Organize the rules to form a rule-based classifier, It explores highly confident associations among multiple attributes and may overcome some constraints introduced by decision-tree induction, which considers only one attribute at a time, Associative classification has been found to be often more accurate than some traditional classification methods, such as C4.5, CBA (Classification Based on Associations: Liu, Hsu & Ma, KDD98), Mine possible association rules in the form of, Cond-set (a set of attribute-value pairs) class label, Build classifier: Organize rules according to decreasing precedence based on confidence and then support, CMAR (Classification based on Multiple Association Rules: Li, Han, Pei, ICDM01), Classification: Statistical analysis on multiple rules, CPAR (Classification based on Predictive Association Rules: Yin & Han, SDM03), Generation of predictive rules (FOIL-like analysis) but allow covered rules to retain with reduced weight, High efficiency, accuracy similar to CMAR, Lazy learning (e.g., instance-based learning): Simply stores training data (or only minor processing) and waits until it is given a test tuple, Eager learning (the above discussed methods): Given a set of training tuples, constructs a classification model before receiving new (e.g., test) data to classify, Lazy: less time in training but more time in predicting, Lazy method effectively uses a richer hypothesis space since it uses many local linear functions to form an implicit global approximation to the target function, Eager: must commit to a single hypothesis that covers the entire instance space. "@type": "ImageObject", ROC curves are often found useful for model selection. "description": "Lazy learning (e.g., instance-based learning): Simply stores training data (or only minor processing) and waits until it is given a test tuple. Prediction using best k rules. "contentUrl": "https://slideplayer.com/slide/4893678/16/images/15/Model+Selection%3A+ROC+Curves.jpg", Upper bound on the expected error rate of the SVM classifier, is independent of the data dimensionality. Specificity = TN\/N. Bagging and boosting can be used to increase overall accuracy by learning and combining a series of individual models. ht _rels/.rels ( J1!}7*"loD c2Haa-?_zwxm { =(GMzc*(a?XT7}*:d/n@kGS6Lu

We think you have liked this presentation. "contentUrl": "https://slideplayer.com/slide/4893678/16/images/9/Classifier+Evaluation+Metrics%3A+Confusion+Matrix.jpg", }. SVM is trained as a convex optimization problem resulting in a unique solution. Model Evaluation and SelectionEvaluation metrics: How can we measure accuracy? "width": "800"

Class Imbalance Problem: One class may be rare, e.g.

The plot also shows a diagonal line. Use validation test set of class-labeled tuples instead of training set when assessing accuracy Methods for estimating a classifiers accuracy: Holdout method, random subsampling Cross-validation Bootstrap Comparing classifiers: Confidence intervals Cost-benefit analysis and ROC Curves 8

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}, 10 ", in each fold is approx. "width": "800" ", Case-based reasoning. At i-th iteration, use Di as test set and others as training set. Test set (e.g., 1\/3) for accuracy estimation.

}, 2 "width": "800" U ^s1xRpbD#rYNrJC.aeD=U]Sik@X6G[:b4(uH%-+0A?t>vT9. "@type": "ImageObject", The nearest neighbor are defined in terms of Euclidean distance, dist(X1, X2) k-NN returns the most common value among the k training examples nearest to xq (test sample) Curse of dimensionality: distance between neighbors could be dominated by irrelevant attributes. "contentUrl": "https://slideplayer.com/slide/4893678/16/images/2/Comparison+of+BPNN+and+SVM.jpg", "@context": "http://schema.org", Error rate = (FP + FN)\/All. ", T1 - Data Mining: Accuracy and Error Measures for Classification and Prediction, BT - Encyclopedia of Bioinformatics and Computational Biology. supports HTML5 video, Published byAlan Gilmore Instance Based Learning K-Nearest Neighbor Algorithm (LVQ) Learning Vector Quantization (SOM) Self Organizing Maps. "description": "Actual Class\\Predicted class.

Instances represented as points in a Euclidean space.

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