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Topic: ID3 algorithm

	ID3 algorithm - Wikipedia, the free encyclopedia
		ID3 (Iterative Dichotomiser 3) is an algorithm used to generate a decision tree.
		The algorithm is based on Occam's razor: it prefers smaller decision trees (simpler theories) over larger ones.
		An explanation of the implementation of ID3 can be found at C4.5 algorithm, which is an extended version of ID3.
	en.wikipedia.org /wiki/ID3_algorithm (266 words)

	Decision Tree Research (Site not responding. Last check: 2007-10-12)
		The algorithm is susceptible to locally optimal convergence, because once it tests an attribute, it eliminates that attribute from the list of possibilities for future tests.
		ID3 prefers trees that are shorter and place the attribute tests that minimize entropy the most near the top of the tree.
		C4.5 and C5 and C5 are extensions to the ID3 algorithm that add pruning to, vary selection criteria from and accept a less strict domain of inputs than ID3.
	www.cs.brandeis.edu /~cs113/classprojects/~lballard/cs113/proj1.html (994 words)

	Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing
		ID3 takes objects of a known class, described in terms of a fixed collection of properties or attributes, and produces a decision tree incorporating these attributes that correctly classifies all the given objects.
		The monetary gain for ID3 was mainly obtained from the high payoffs for a few races, i.e., $24.4 for race 31, $41.20 for race 59, and $26.8 for race 83.
		Both algorithms appeared to be more robust than humans in their ability to analyze the large set of racing data objectively and reach unbiased conclusions.
	ai.bpa.arizona.edu /papers/dog93/dog93.html (6253 words)

	[No title] (Site not responding. Last check: 2007-10-12)
		ID3 uses the concept of entropy as a quantitative measure of the "uncertainty" of classification induced by an attribute.
		A genetic algorithm is a global search technique as it does not look at a single point from where to expand the search for optima, but it evolves a population of points towards the optimum solution.
		The quality of the rules in the classifier pool is improved by applying genetic algorithms to the population of classifiers, with the classifier strength used as their fitness value.
	bradley.bradley.edu /~chris/machinelearn.html (4029 words)

	ID3 (Site not responding. Last check: 2007-10-12)
		ID3 stands for Induction of Decision Trees, and is a supervised learning system that constructs decision trees from a set of examples.
		However, if the examples are supplied one at a time, the ID3 algorithm can still be used, but it would construct a new decision tree from scratch, every time a new example is observed.
		For such serial learning tasks, one would prefer an incremental algorithm, on the assumption that it is more eficient to revise an existing tree than it is to generate a new tree every time.
	pascal.iseg.utl.pt /~sie/Aula05/ID3.htm (2019 words)

	[No title]
		The obtained binary tree is printed out using the algorithm which is usually used to print directory structures - each next level in the tree is idented, with leaves in the tree having maximal indentation in any given path from a root down.
		The algorithm is a straitforward one and it is relatively easy to implement this algorithm of datamining.
		There is also a possibility for improving the time performance of the algorithm by creating special datastructures in memory for keeping track of separate data sets (in our implementation the track was kept by a special field in our table).
	www.pa.uky.edu /~sorokin/stuff/cs685/submit/id3.doc (977 words)

	DMS Tutorial - Decision trees (Site not responding. Last check: 2007-10-12)
		Most algorithms that have been developed for learning decision trees are variations on a core algorithm that employs a top-down, greedy search through the space of possible decision trees.
		The central focus of the decision tree growing algorithm is selecting which attribute to test at each node in the tree.
		The estimation criterion in the decision tree algorithm is the selection of an attribute to test at each decision node in the tree.
	dms.irb.hr /tutorial/tut_dtrees.php (2828 words)

	University of Missouri - Rolla, Computer Science (Site not responding. Last check: 2007-10-12)
		The nonsymmetric scheduling algorithm is based on the rate monotonic scheduling algorithm which assigns higher task priorities to tasks with shorter release periods.
		An offline algorithm is developed and tested as a basis for comparison, and a simple on-line algorithm is developed as an incremental step toward the final algorithm, proportional on-line iterative improvement.
		The performance of each of the algorithms was experimentally evaluated in numerous realistically simulated process control situations consisting of flow, level, and temperature control loops with various values of dead-time and process noise.
	www.cs.umr.edu /technicalreports1992.html (4306 words)

	[No title]
		ID3 is a program for inducing classification rules in the form of decision trees from a set of classified examples.
		A learning algorithm that searches for hypotheses larger than necessary (i.e., that does not use the Occam's Razor Principle) is normally "under- constrained"; it does not know, based on the training examples only, which of the many competitive hypotheses of the large size should be inferred.
		If ID3 is applied to the concepts that fit ANN's bias, especially if their hyperplanes are not perpendicular to any axis, then many zigzag hyperplanes that are perpendicular to axes would be needed to separate different classes of the examples.
	www.cs.cmu.edu /Groups/AI/util/pubs/journals/jair/volume1/sources/ling94a.txt (8935 words)

	Building Classification Models: ID3 and C4.5
		ID3 and C4.5 are algorithms introduced by Quinlan for inducing Classification Models, also called Decision Trees, from data.
		C4.5 is an extension of ID3 that accounts for unavailable values, continuous attribute value ranges, pruning of decision trees, rule derivation, and so on.
		The ID3 algorithm is used to build a decision tree, given a set of non-categorical attributes C1, C2,.., Cn, the categorical attribute C, and a training set T of records.
	www.cis.temple.edu /~ingargio/cis587/readings/id3-c45.html (2078 words)

	Genetic Decision Tree Building (Site not responding. Last check: 2007-10-12)
		ID3 is a greedy algorithm which leaves us with little to do if we are unhappy with the results of the resulting tree because each run will result with the same tree.
		ID3 is computationally severe because it requires re-computation of the entropy recursively as we work our way down the tree.
		We have looked at several articles which discuss the possibility of using a genetic algorithm to build trees and they all explain how it is possible but none of them explicitly explained how to accomplish the blending of the trees nor claimed to have actually implemented such an algorithm.
	nand.net /~paras/genetic_decision_trees (1591 words)

	Machine Learning/Inductive Inference/Decision Trees/Construction (Site not responding. Last check: 2007-10-12)
		The central focus of the ID3 algorithm is selecting which attribute to test at each node in the tree.
		ID3 can be easily extended to handle noisy training data by modifying its termination criterion to accept hypotheses that imperfectly fit the training data.
		However, the algorithm may be modified to consider additional levels of the tree, at a cost of a higher power of n.
	www.cs.uregina.ca /~hamilton/courses/831/notes/ml/dtrees/4_dtrees2.html (893 words)

	[No title] (Site not responding. Last check: 2007-10-12)
		Description of Algorithm The ID3 algorithm, developed by Quinlan [1986b], is a non-incremental inductive "classification" algorithm.
		The ID3 algorithm is non-incremental in the sense that it assumes the entire training set is available at the beginning of the algorithm.
		For both kinds of nodes, the table of instance counts required by the ID3 algorithm and classification errors at each node is kept on the property list of the node.
	www.cise.ufl.edu /~fu/Teach/Learn/id3.txt (1887 words)

	[No title]
		Decision tree induction — Basic Decision tree Algorithms - ID3 and C4.5 The basic algorithm for decision tree induction is a greedy algorithm that constructs decision trees in a top-down recursive divide-and-conquer manner.
		As the algorithm only considers the current best gain it misses horizon effects, where an optimal tree may be built by selecting an attribute that is not the best at some stage.
		With current data mining algorithms there exists the capability to identify or extract data, which is most relevant, and provide analysts with different "views" of the data to aid in their analysis.
	www.cs.uiowa.edu /~kghoshda/MEthesis.doc (13387 words)

	ID3 (Site not responding. Last check: 2007-10-12)
		ID3 uses information gain to help it decide which attribute goes into a decision node.
		ID3 is a nonincremental algorithm, meaning it derives its classes from a fixed set of training instances.
		The classes created by ID3 are inductive, that is, given a small set of training instances, the specific classes created by ID3 are expected to work for all future instances.
	www.cise.ufl.edu /~ddd/cap6635/Fall-97/Short-papers/2.htm (1216 words)

	Training Decision Trees with ID3 (Site not responding. Last check: 2007-10-12)
		The purpose of our project was to apply a decision-tree learning algorithm (ID3) to some sample training sets of data and evaluate how accurately each decision tree generalized to a sample testing set of data.
		Then it loads the ID3 algorithm files and one of the data files (the others are commented out).
		To test the decision tree created by ID3, enter (test-all ), where is any of the training or test data lists from the converted data files.
	www.css.tayloru.edu /~jcoombs/proj/ml/id3.htm (668 words)

	The Machine Learning Dictionary
		A covering algorithm, in the context of propositional learning systems, is an algorithm that develops a cover for the set of positive examples - that is, a set of conjunctive expressions that account for all the examples but none of the non-examples.
		For example, in ID3, the observation language would be the notation used to represent the training instances, including attributes and their allowable values, and the way instances are described using attributes.
		The point of the ID3 algorithm is to decide the best attribute, out of those not already used, on which to split the training instances that are classified to a particular branch node.
	www.cse.unsw.edu.au /~billw/mldict.html (6457 words)

	Best Download mp3 - Downloads
		The differences between the layers are caused by the different psychoacoustic models used by them; the Layer 1 algorithm is typically substantially simpler, therefore a higher bit rate is needed for transparent encoding.
		Due to the fact that their lossy encoding loses information, MP3 algorithms work hard to ensure that the parts lost cannot be detected by human listeners by modeling the general characteristics of human hearing (e.g., due to noise masking).
		Implementors of the standard were supposed to devise their own algorithms suitable for removing parts of the information in the raw audio (or rather its MDCT representation in the frequency domain).
	www.mp3.bestdownload.biz (3573 words)

	Initializing Our ID3 Table (Beginning Visual Basic 6 Database Programming)
		This is the table that we will use for generating the results of the ID3 algorithm.
		We can use our ID3 algorithm to decide if the country, region, or language is important to new sales for each individual product.
		As mentioned, we could have just run the ID3 algorithm on the data as-is. But this simply illustrates how you might be able to add simple meta-data to provide additional insight on the underlying data.
	msdn.microsoft.com /library/en-us/dnbegvb/html/initializingourid3table.asp?frame=true (821 words)

	Dr. Dobb's \| Algorithm Alley \| July 22, 2001 (Site not responding. Last check: 2007-10-12)
		The ID3 algorithm searches through the attributes of a dataset for the one that conveys the most information about the desired target.
		Rather than copying the data table for each partition, I pass the partially formed data tree to the routine that calculates negentropy, allowing the program to exclude records that are not relevant for that part of the tree.
		The first is a set of sample data from a botanical classification problem, in which a type of flower, an iris, is to be classified into one of two subgenera (Virgin, Setosa) according to the dimensions of sample pistils and stamens.
	www.ddj.com /documents/s=960/ddj9606m/9606m.htm (2453 words)

	[No title]
		The main weakness of this algorithm is the generation of sharp decision boundaries at every node within the tree.
		The Genetic Algorithm (GA) [8] is a stochastic searching algorithm inspired by the process of natural evolution and has been used successfully in the area of function optimisation problems.
		The FIA Algorithm FIA first requires the generation of a decision tree [9] to be induced from a training set of examples in-order to construct a fuzzy rule base.
	www.doc.mmu.ac.uk /docs/keeleys/mexico2.doc (763 words)

	Analysis of Data Mining Algorithms
		The ID3 algorithm (Quinlan86) is a decision tree building algorithm which determines the classification of objects by testing the values of the their properties.
		As expected, the execution times of all the algorithms increase as the support is decreased because of a large increase in the number of large sequences in the result.
		Averaging an algorithm's performance over all target concepts, assuming they are all equally likely, would be like averaging a car's performance over all possible terrain types, assuming they are all equally likely.
	www-users.cs.umn.edu /~desikan/research/dataminingoverview.html (5928 words)

	COSI113B Decision Tree Learning (Site not responding. Last check: 2007-10-12)
		A learning algorithm for a Decision Tree is an algorithm that can takes a global question as well as example data that relates to that question and translate that data into a Decision Tree which answers the question.
		Generally in a decision tree learning problem one assumes that the questions (or attributes) and their possible answers are given, as determining the attributes a in the general case is clearly AI complete.
		Since we can consider any algorithm which generates a Decision Tree in the above way to be searching through the space of Decision Trees we know by the No Free Lunch Theorems that ultimately we cannot create a super algorithm that is perfect in all cases.
	www.cs.brandeis.edu /~cs113/classprojects/~kristian/cs113/DecisionTrees.html (2003 words)

	Machine learning with variables
		A companion document Pseudo-code for ID3 describes data structures and algorithmic structure for ID3.
		The ID3 algorithm is dealt with in chapter 4, Machine Learning, pages 90-95.
		Both the ID3 algorithm and standard databases are based on tuples, each of which consists of a number of attributes with values.
	www.hiraeth.com /books/ai96/QBB/var.html (758 words)

	ML (Site not responding. Last check: 2007-10-12)
		This point is addressed by Quinlan's ID3 algorithm which is an enhancement of the classification algorithm.
		After repeating for the all available attributes (hair colour and eye colour at the root) the maximum expected information gain is selected and this is the attribute that is chosen to add to the decision tree.
		Using the training set of figure 3.06 and applying the classification algorithm by selecting the attributes using the information theoretic heuristic described above the decision tree in figure 3.10 is generated.
	www.cs.mdx.ac.uk /staffpages/serengul/The.ID3.algorithm.htm (1035 words)

	[No title]
		Quinlan(1), is the usual reference but the article in Byte gives a highly readable description of the algorithm, very much in the Byte mould.
		ID3 is best suited to problems involving symbols, but attributes can be integers as well, (positive and negative).
		The algorithm may consequently be unable to completely classify all the examples.
	www.cogs.susx.ac.uk /users/christ/pub/help/id3_chisqd (1415 words)

	[No title]
		Key Choices of ID3 The Key Choices for users of ID3 style algorithms are: Properties, Leaf Nodes and the Root Property from where the tree starts.
		Overcoming ID3 Type Deficiencies There are several methods that can be used to limit the effects of the ID3 based deficiencies, both before and after the construction of the decision tree.
		From the exhaustive research into inductive learning algorithms generally, it is clear that learning samples containing small proportions of missing attribute data can be tolerated by having the missing attribute data examples truncated from the sample training set.
	www.injunea.demon.co.uk /degree/lisp/aicw4.doc (993 words)

	EECS 492 Problem Set 7 (Site not responding. Last check: 2007-10-12)
		ID3 was the first widely used DTL algorithm, and is representative of a group of learning algorithms known as the Top-Down Induction of Decision Trees (TDIDT) family.
		You will be provided with an implementation of the ID3 algorithm, as well as some simple testing functions.
		Your task will be to analyze the behavior of ID3 and perform a number of experiments to verify your understanding of the power and limitations of decision-tree learning.
	ai.eecs.umich.edu /people/wellman/courses/eecs492/f95/PS7.html (2143 words)

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