The Universality of Nearest Neighbor Sets in Classification and Prediction Dr. William Perrizo, Dr. Gregory Wettstein, Dr. Amal Shehan Perera and Tingda Lu Computer Science North Dakota State University Fargo, ND 58108 USA Example: Medical Expert System (Ask a Nurse) Symptoms plus past diagnoses are collected into a table called CASES. For each undiagnosed new_symptoms,CASES is searched for matches: SELECT DIAGNOSIS FROM CASES WHERE CASES.SYMPTOMS = new_symptoms If there is a predominant DIAGNOSIS, then report it elseIf there's no predominant DIAGNOSIS, then Classify instead of Query i.e., find the fuzzy matches (near neighbors) SELECT DIAGNOSIS FROM CASES WHERE CASES.SYMPTOMS ≅ new_symptoms else call your doctor in the morning Almost universally, Decision Making is, consulting a database of past expert decisions (your own or that of other experts)) Past similar decisions are collected into a table called CASES (either explicitly or in one’s head). For each new decision CASE, search for descriptive feature matches (or near matches). Decide based on the predominant case. Sometimes this is called “CASE-BASED REASONING”. SELECT CASE FROM CASES WHERE CASES.DESCRIPTIVE_FEATURES = [or ~=] new_descriptive_features If there is a predominant CASE, then report it elseIf there's no predominant CASE, then Classify instead of Query i.e., find the fuzzy matches (near neighbors) SELECT DIAGNOSIS FROM CASES WHERE CASES.DESCR_FEATURES = [or ~=] new_descr_features else make default decision. Near Neighbor Classification Given a (large) TRAINING SET T(A1, ..., An, C) with FEATURES A=(A1,...,An), C CLASS, C, and Classification of an unclassified sample, (a1, ..., an) is just: SELECT Max (Count (T.Ci)) FROM T WHERE T.A1=a1 AND T.A2=a2 ... AND T.An=an GROUP BY T.C; i.e., It is just a SELECTION, since C-Classification is assigning to (a1, ..., an) the most frequent C-value in RA=(a1, ..., an). But, if the EQUALITY SELECTION is empty, then we need a FUZZY QUERY to find NEAR NEIGHBORs instead of exact matches. That's Nearest Neighbor Classification (NNC). Based on the definition of “Near” Essentially all classification and prediction algorithms are nearest neighbour vote based algorithms. Data mining • Data mining, has 3 general methodologies for extracting information and knowledge from data. – Rule Mining: Strong antecedent consequent relationships among the subsets of the column attributes. – Classification & Prediction: Discovering signatures for the individual values in a specified column (class attribute) or attribute from values of the other attributes (feature attributes). – Clustering: using some notion of tuple similarity to group together training table rows so that within a group (a cluster) there is high. Prediction and Classification The Classification and Prediction problem is a very interesting data mining problem. The problem is to predict a class label based on past (assumed correct) prediction activities. Typically the training datasets of past predictions are extremely large (which is good for accuracy but bad for speed of prediction). Immediately one runs into the famous problems, the curse of cardinality and the curse of dimensionality. The curse of cardinality if the number of horizontal records in a training file is very large, standard vertical processing of horizontally record structures can take an unacceptably long time (e.g., if there are millions or billions of horizontal records to scan); and the gold standard method prediction/classification method, k-Nearest Neighbor, will not yield the most accurate result unless a second scan is made. Thus the curse of cardinality is both a time curse and an accuracy curse. The curse of cardinality The curse of cardinality is a very serious problem for Near Neighbor Classifiers (NNCs) of horizontal data (speed and accuracy). NNCs for horizontally structured data require a scan of the entire training set to determine the [k] nearest neighbors Take records 1,2,...,k as the initial k Nearest Nbr set (kNN set) Get the k+1st record. If it is closer than any one in the kNN set, replace. Get k+2nd. If it is closer than any one in the kNN set, replace. Get k+3rd ... ……) With horizontally structured data and only one vertical scan, the best one can do is determine one of many “k nearest neighbor sets” i.e., there may be many training points that are just as near as the kth one selected). Our solution is to use a vertical data organization so that one horizontal scan yields the Closed KNN set (CKNN) immediately (all neighbors within a given distance). Cost is dependent on the number of attributes This ameliorates the Curse of Cardinality problem considerably (PA-KDD 2003 paper). Curse of Cardinality - 2 With horizontally structured data, he only way to get a fair classification vote in which all neighbors at a given distance get equal vote, is to make a second vertical scan of the entire training set, which is expensive. For that reason, most kNNC implementations on horizontally structured data disregard the other neighbors at the same distance from the unclassified sample, not because the k voters suffice, but because it is too expensive to find the other neighbors at that same distance and to enfranchise them for voting as well. Of course, if the training set is such that any neighbor gives a representative vote for all neighbors at that same distance, then kNN is just as good as Closed kNN. But that would have to mean that all neighbors at the same distance have the same class, which means the classes are concentric rings around the unclassified sample. If that is known, no sophisticated analysis is required to classify. So, we solve the curse of cardinality (speed and accuracy) by using vertical data. However, it is more common to ”solve” the curse of cardinality by resorting to model-based classification (in which, first, a compact model is built to represent the training set information (the training phase) then that closed form, compact model is used over and over again to classify and predict (the prediction phase). k-Nearest Neighbor Classification (kNNC) and closed-k-Nearest Neighbor Classification (ckNNC) 1) Select a suitable value for k 2) Determine a suitable distance or similarity notion (definition of near) 3) Find the k nearest neighbor set [closed] of the unclassified sample. 4) Find the plurality class in the nearest neighbor set. 5) Assign the plurality class as the predicted class of the sample T Let T be the unclassified point or sample. Using Euclidean distance and k = 3: Find the 3 closest neighbors. Move out from T until ≥ 3 neighbors are found. That's 216 !(more than 3NN arbitrarily select 3 !) one point from this boundary line as the 3rd nearest neighbor, whereas, C3NN includes all points on this boundary line. closed k Nearest Neighbor Classification (ckNNC) Often yields much higher classification accuracy than traditional kNNC. At what additional cost? Actually, it can be at a negative additional cost i.e., It can be faster AND more accurate when using vertical data organization (because it yields the closed nearest neighbor set with just one horizontal scan of the vertical data structures). The NEXT SLIDE describes our Ptree vertical data organization which facilitates this faster and more accurate ckNNC.) Predicate tree technology: vertically project each attribute, then vertically project each bit position of each attribute, then compress each bit slice into a basic Ptree. e.g., compression of R11 into P11 goes as follows: Current practice: Structure data into horizontal records. Process vertically (scans) Base 2 Base 10 R(A1 Horizontally structured records Scanned vertically 2 6 3 2 3 2 7 7 R[A1] R[A2] R[A3] R[A4] A2 A 3 A4 ) 7 7 7 7 2 2 0 0 6 6 5 5 1 1 1 1 1 0 1 7 4 5 4 4 = 010 011 010 010 011 010 111 111 111 111 110 111 010 010 000 000 110 110 101 101 001 001 001 001 001 000 001 111 100 101 100 100 R11 0 0 0 0 0 0 1 1 pure1? false=0 pure1? true=1 Top-down construction of the 1-dimensional Ptree representation of R11, denoted, P11, is built by recording the truth of the universal predicate pure 1 in a tree recursively on halves (1/21 subsets), until purity is achieved. pure1? false=0 pure1? false=0 pure1? false=0 1. Whole is pure1? false 0 2. Left half pure1? false 0 3. Right half pure1? false 0 4. Left half of rt half ? false0 5. Rt half of right half? true1 010 011 010 010 011 010 111 111 111 111 110 111 010 010 000 000 110 110 101 101 001 001 001 001 001 000 001 111 100 101 100 100 R11 R12 R13 R21 R22 R23 R31 R32 R33 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 R41 R42 R43 0 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 Horizontally AND basic Ptrees But it is pure (pure0) so this branch ends P11 P12 P13 P11 0 0 0 01 1 0 0 0 0 0 0 0 01 1 P21 P22 P23 P31 P32 P33 P41 P42 P43 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 01 0 0 0 0 1 0 1 0 0 0 0 10 10 10 01 00 00 0001 0100 ^ 10 ^ ^ ^ 01 ^ ^ ^ 10 01 01 01 01 Curse of Dimensionality Curse of dimensionality: As files get very wide (have many columns) Intuition breaks down The critical notion of “near” ceases to work in Near Neighbor Classification. Why? To what limit does the volume of a unit disk go, as n --> ? For n=1, the volume is For n=2, the volume is π r2 or 2 (length of line from -1 to 1). ~3.1416 For n=3, the volume is 4/3 π r3 ~4.1888, etc. Intuition might tell us That volume is heading toward as n goes to . Or that it will top out asymptotically at some number > 4.1888. The volume of the unit disk goes to 0 as n --> , Reaching its maximum at dimension = 5. -1 +1 Therefore, there are almost no [Euclidean distance] Nbrs in high Dimensions 2 -1 21=2 +1 2 The volume of the unit disk goes to 0 as n --> , with max at dim = 5. But the volume of the unit cube goes to as n --> . 2 22=4 This is not intuitive to most people It has consequences in NNC. This tells us that there's a lot of volume in a high dimensional unit cube, but, in its inscribed unit disk, there is almost nothing (no “space”, no volume, and 23=8 therefore no points (relatively speaking). ). E.g, there are lots of potential near neighbors in a high dimensional unit cube. Thus, when using, e.g., L1 or Hamming distance to define neighborhoods, there are plenty of neighbors, but there are almost no near neighbors in a high dimensional unit disk when using Euclidean distance. Our solution: use cubes, not disks as neighborhoods (this is typically the distance of choice in Genomics, Bioinformatics and BioMedical Informatics). Ptree vertical technology facilitates the construction and analysis of Cube neighborhoods. 16 32 64 . . . 2n . . . All classification is Near Neighbor Classification ? NNC approach takes into consideration typical training data set size characteristics, which cause: cardinality (curse of cardinality) dimension (curse of dimensionality) Model-based classifiers are [often less accurate, in general, and are really] Near Neighbor Classifiers using an alternate idea of “near”. We conclude that the improved speed of CNNC using the Ptree vertical approach a good choice since It retains (improves) the speed. It retains (improves) the accuracy of the Near Neighbor approach. Near Neighbor Classification Given a (large) TRAINING SET T(A1, ..., An, C) with FEATURES A=(A1,...,An), C CLASS, C, and Classification (in psuedo-SQL) of an unclassified sample, (a1, ..., an) is just: SELECT Max (Count (T.Ci)) FROM T WHERE T.A1=a1 AND T.A2=a2 ... AND T.An=an GROUP BY T.C; i.e., It is just a SELECTION, since C-Classification is assigning to (a1, ..., an) the most frequent [neighboring] C-value in RA=(a1, ..., an). If the EQUALITY SELECTION is empty, then we need a FUZZY QUERY to find NEAR NEIGHBORs instead of exact matches. That's Nearest Neighbor Classification (NNC). Nearest Neighbor Classification (sample-based) and Eager Classification (model-based) Given a TRAINING SET, R(A1,..,An, C), with C = CLASSES and (A1,...,An)=FEATURES Nearest Neighbor Classification (NNC) Selecting a set of R-tuples with similar features Letting the corresponding class values vote. Nearest Neighbor Classification won't work very well if The vote is inconclusive (close to a tie) or if similar (near) is not well defined, Then we build a MODEL of TRAINING SET (at, possibly, great 1-time [build phase] expense?) When a MODEL is built - Eager classification uses the model to assign class. Model-less methods like Nearest Neighbor - Lazy or Sample-based. Eager Classification (model-based) Eager Classifiers models examples Decision trees, Probabilistic models (Bayesian Classifier, Neural Networks, Support Vector Machines, etc.) How do you decide when an EAGER model is good enough to use? How do you decide if a Nearest Neighbor Classifier is working well enough? We have a TEST PHASE. Typically, we set aside some training tuples as a Test Set (Test tuples cannot be used in model building or and cannot be used as nearest neighbors). If the classifier passes the test (a high enough % of Test tuples are correctly classified by the classifier) it is accepted. EXAMPLE: Computer Ownership The TRAINING SET for predicting who owns a computer is: Customer ( Age | 24 | 58 | 48 | 58 | 28 Salary | 55,000 | 94,000 | 14,000 | 19,000 | 18,000 Job | Programmer | Doctor | Laborer | Domestic | Builder Owns Computer ) | yes | | no | | no | | no | | no | A Decision Tree (model) classifier built from TRAINING: Is this a Near Neighbor Classifier? Where are Near Neighborhoods involved? Training subset at the bottom of each decision path represents a near neighborhood of any unclassified sample that traverses the decision tree to that leaf. The concept of “near” or “highly correlated” : The unclassified sample meets the same set of conditions or criteria as the near neighbors at the bottom of that path of condition criteria. We are using a different (accumulative) “correlation” definition along each branch of the decision tree and the subsets at the leaf of each branch are true Near Neighbor sets for the respective correlations or notions of nearness. Neural Network classifiers Neural Network classifier Training Adjusting the weights and biases Through back-propagation Until an acceptable performance. Matrix of weights and biases – Determiners of our near neighbor sets. We continue to train by adjusting weights and biases until: Near neighbor sets – inputs producing same class Are sufficiently “near” to each other To give us a level of accuracy. Support Vector Machine (SVM) classifiers The very first step in Support Vector Machines (SVM) classification is To isolate a neighborhood in which to examine the boundary and the margins of the boundary between classes (assuming a binary classification problem). Thus, Support Vector Machines are Nearest Neighbor Classifiers also. CONCLUSIONS AND FUTURE WORK We have made the case that classification and prediction algorithms are nearest neighbor vote classification and predictions. The conclusion depends upon how one defines “near”. We have shown that there are clearly “nearness” or “correlations” or “similarities that provide these definitions. Broadly speaking, this (NNC) is the way we always proceed in Classification. Faced with a classification or prediction problem? Head off the standard way of approaching Classification, That of using a model-based classification method unless it just doesn’t work well enough and only then using Nearest Neighbor Classification. “It is all Nearest Neighbor Classification” That standard NNC should be used UNLESS it takes too long. Only then should one consider giving up accuracy (of your near neighbor set) for speed by using a model (Decision Tree or Neural Network). With a vertical data structure like P-trees NNC can be applied in most cases efficiently.