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Published bySilvester Lindsey Modified over 4 years ago

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Navneet Goyal

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Instance Based Learning Rote Classifier K- nearest neighbors (K-NN) Case Based Resoning (CBR)

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Classification: Eager & Lazy Learners Decision Tree classifier is an example of an “eager learner” Because they are designed to learn a model that maps the input attributes to the class label as soon as the training data becomes available An opposite strategy would be to delay the process of modeling the training data until it is needed to classify the test examples LAZY Learners

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Classification: Eager & Lazy Learners Rote classifier is an example of lazy learner, which memorizes the entire training data & performs the classification only if the attributes of a test instance matches exactly with one of the training examples Drawback: Cannot classify a new instance if it does match any training example

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Classification: Nearest Neighbors To overcome this drawback, we find all training examples that are relatively ‘similar’ to the test example Examples, which are known as ‘Nearest Neighbors’ can be used to determine the class label of the test example If it walks like a duck, quacks like a duck, and looks like a duck, then it is probably a duck

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Nearest Neighbor Classifiers Training Records Test Record Compute Distance Choose k of the “nearest” records

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Classification: Nearest Neighbors Each test example is represented as a point in a d -dimensional space For east test example we use a proximity measure K-nearest neighbors of a given example z refer to the k points that are closest to z

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Classification Using Distance Place items in class to which they are “closest”. Must determine distance between an item and a class. Classes represented by Centroid: Central value. Medoid: Representative point. Individual points Algorithm: KNN

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Definition of Nearest Neighbor K-nearest neighbors of a record x are data points that have the k smallest distance to x

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Nearest-Neighbor Classifiers l Requires three things –The set of stored records –Distance Metric to compute distance between records –The value of k, the number of nearest neighbors to retrieve l To classify an unknown record: –Compute distance to other training records –Identify k nearest neighbors –Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote)

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Nearest Neighbor Classification Compute distance between two points: Euclidean distance Determine the class from nearest neighbor list take the majority vote of class labels among the k-nearest neighbors Weigh the vote according to distance weight factor, w = 1/d 2

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Nearest Neighbor Classification… Choosing the value of k: If k is too small, sensitive to noise points If k is too large, neighborhood may include points from other classes

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Nearest Neighbor Classification… Problem with Euclidean measure: High dimensional data curse of dimensionality Can produce counter-intuitive results 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 vs d = 1.4142 Solution: Normalize the vectors to unit length

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Nearest neighbor Classification… Part of more general technique called as ‘Instance-based learning’ Require a proximity measure k-NN classifiers are lazy learners It does not build models explicitly Unlike eager learners such as decision tree induction and rule-based systems Classifying unknown records are relatively expensive

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Nearest neighbor Classification… Make their classification based on local information, where as DT & rule-based classifiers attempt to find global model that fits the entire input space As decisions are made locally, they are quite susceptible to noise (for small k) Can produce wrong results unless the appropriate proximity measure and data preprocessing steps are taken

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Nearest Neighbor Classification… Scaling issues Attributes may have to be scaled to prevent distance measures from being dominated by one of the attributes Example: height of a person may vary from 1.5m to 1.8m weight of a person may vary from 90lb to 300lb income of a person may vary from $10K to $1M Proximity measure may be dominated by differences in weights and income of a person

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Example: PEBLS PEBLS: Parallel Examplar-Based Learning System (Cost & Salzberg) Works with both continuous and nominal features For nominal features, distance between two nominal values is computed using modified value difference metric (MVDM) Each record is assigned a weight factor Number of nearest neighbor, k = 1

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Example: PEBLS Class Marital Status SingleMarriedDivorced Yes201 No241 Distance between nominal attribute values: d(Single,Married) = | 2/4 – 0/4 | + | 2/4 – 4/4 | = 1 d(Single,Divorced) = | 2/4 – 1/2 | + | 2/4 – 1/2 | = 0 d(Married,Divorced) = | 0/4 – 1/2 | + | 4/4 – 1/2 | = 1 d(Refund=Yes,Refund=No) = | 0/3 – 3/7 | + | 3/3 – 4/7 | = 6/7 Class Refund YesNo Yes03 No34

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Example: PEBLS Distance between record X and record Y: where: w X 1 if X makes accurate prediction most of the time w X > 1 if X is not reliable for making predictions

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