UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA ECML PKDD 2008 15-19 September 2008, Antwerp, Belgium A Genetic Algorithm for Text Classification Rule Induction A.Pietramala1, V.Policicchio1, P.Rullo1,2, I.Sidhu3 Università della Calabria (Rende, Italy) 1. {a.pietramala,policicchio,rullo}@mat.unical.it 2. 3. Exeura Srl (Rende, Italy) Kenetica Ltd (Chicago, IL-USA) {isidhu}@computer.org UNIVERSITA’ DELLA CALABRIA Outline Dipartimento di MATEMATICA – Motivations – The Olex Hypothesis Language – The Genetic Algorithm Approach (Olex-GA) – Experimental Results and Comparative Evaluation – Discussions – Conclusions and Future Work A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Motivations • Rule learning algorithms have become a successful strategy for classifier induction. • Rule-based classifiers provide the desirable property of being readable and, thus, easy to understand (and, possibly, modify). • Genetic Algorithms (GAs) are stochastic search methods inspired to the biological evolution. • GAs show the capability to provide good solutions for classical optimization tasks (e.g. TSP and Knapsack) A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Rule Induction and GAs • Rule induction is one of the application fields of GAs. The basic idea is that: – Each individual in the population represents a candidate solution (a classification rule or a classifier) – The fitness of an individual is evaluated in terms of the predictive accuracy. • We propose presents a GA approach, called Olex-GA, for the induction of rule-based text classifiers. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Olex-GA The hypothesis language Dipartimento di MATEMATICA • A classifier Hc (Pos,Neg) is of the form: Hc (Pos,Neg) Pos c ( t1 d ... tn d ) ( tn 1 d ... tn m d ). c category ti term (n-gram) d document Neg “if any of the terms t1,…,tn occurs in d and none of the terms tn+1,…,tn+m occurs in d, then classify d under category c” A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction Olex-GA The hypothesis language UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA • The terms in Pos and Neg are chosen among the ones belonging to the local vocabulary: V (k , f ) UcC Vc (k , f ) • Intuitively, Vc (k, f ) is the set of the best k terms for category c according to a given scoring function f. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Olex-GA Problem statement • The Olex-GA’s learning problem is stated as an optimization problem: PROBLEM MAX-F Let a category c C and a vocabulary V (k, f) over the training set TS be given. Then, find two subsets of V (k, f), Pos = {t1,…,tn } and Neg = {tn+1,…,tn+m } with Pos ≠ Ø , such that Hc (Pos, Neg) applied to TS yields a maximum value of Fc, (over TS), for a given [0,1]. • Problem MAX-F is NP-Hard. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Olex-GA A Genetic Algorithm to Solve MAX-F • Problem MAX-F is a combinatorial optimization problem aimed at finding a best combination of terms taken from a given vocabulary. • MAX-F is a typical problem for which GAs are known to be a good candidate resolution method. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA GA-Olex Our implementation of GA • In the following, we describe our choices concerning: – Population Encoding – Fitness Function – Evolutionary Operators A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction Olex-GA Population Encoding UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA • Each individual represents an entire classifier. EXAMPLE Given a vocabulary V ( k ,f ) { t1 ,t2 ,t3 ,t4 ,t5 } Hc c ( t1 t3 ) ( t2 t4 ) K 1 0 1 0 0 0 1 0 1 0 t1 t2 t3 t4 t5 t1 t2 t3 t4 t5 • An individual is simply a binary representation of the sets Pos and Neg of a classifier Hc (Pos, Neg) . A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Olex-GA Population Encoding • We restrict the search of both positive and negative terms, respectively, to: – Pos*, the set of terms belonging to Vc (k, f ) (candidate positive terms); – Neg*, the set of terms which occur in any document containing some candidate positive term and not belonging to the training set TSc of c (candidate negative terms). • The reduction of search space allows: – an improvement of the algorithm efficiency – a quick convergence toward good solutions A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Olex-GA Fitness Function • The fitness of a chromosome K, representing Hc(Pos,Neg) is the value of the F-measure resulting from applying Hc(Pos,Neg) to the training set TS. • This choice naturally follows from the formulation of problem MAX-F. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Olex-GA Evolutionary Operators • We perform: – selection via the roulette-wheel method, – crossover by the uniform crossover scheme. – mutation, which consists in the flipping of each single bit with a given (low) probability. – elitism, in order to ensure that the best individuals of the current generation are passed to the next one without being altered A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Olex-GA Experimentation We have experimentally evaluated our algorithm on two standard benchmark corpora: • REUTERS-21578 (R10) – It consists of 12,902 documents – They are manually classified with respect to 135 categories. We have considered the subset of the 10 most populated categories. • OHSUMED – We used the collection consisting of the first 20,000 documents from the 50,216 medical abstracts of the year 1991. – The classification scheme consisted of the 23 MeSH disease categories. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Experimental settings • We applied the stratified holdout method: REUTERS: – ModApté split : 9603 documents are used to form the training corpus (seen data) and 3299 to form the test set (unseen data). OHSUMED: – The first 10,000 were used as seen data and the second 10,000 as unseen data. In both cases, we have randomly split the set of seen data into a – training set (70%), on which to run the GA – and a validation set (30%), on which tuning the model parameters. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Experimental settings Dipartimento di MATEMATICA • GA Parameters: Parameter Value Iterations 3 Population Size 500 Num of Generations 200 Cross-over Rate 1.0 Mutation Rate 0.001 Elitism Probability 0.2 • For each chromosome K in the population, we initialized K+ at random, while we set K¡- [t] = 0, for each t Neg* (thus, K initially encodes a classifier Hc(Pos,Neg) with no negative terms). A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Comparative Evaluation • On both corpora, we carried out a direct comparison with the following systems: – SVM (both polynomial and radial basis function) – Ripper (with two optimization steps) – C4.5 – Naive Bayes – Olex-Greedy • The performances were evaluated using the Weka library of ML algorithms (apart from Olex-Greedy). A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA • Performance Comparison on Reuters Efficacy – SVMpoli > SVMrbf > Ripper ≈ Olex-GA > C45 > Olex-Greedy > NB • Efficiency – NB > Olex-Greedy > SVMpoli > Olex-GA > C45 > SVMrbf > Ripper A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA • Performance Comparison on OHSUMED Efficacy – Olex-GA > Ripper > SVMpoli > Olex-Greedy > SVMrbf ≈ NB > C45 • Efficiency – NB > Olex-Greedy > SVMpoli > Olex-GA > C45 > SVMrbf > Ripper A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction Discussions – UNIVERSITA’ DELLA CALABRIA Dipartimento di MATEMATICA Relation to other inductive rule learners • Conventional Rule Learners (Ripper, C4.5): – Usually rely on a two-stage process: rule induction and rule pruning. – Each of the above step in turn consists of several steps • Olex-GA relies on a a single-step process which does not need any post-induction optimization. • With respect to Olex-Greedy, Olex-GA provides better predictive accuracy, but is less efficient. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Conclusions Dipartimento di MATEMATICA • Olex-GA encodes a classifier, in a very natural and compact way, as an individual • Fitness of an individual is evaluated as the F-measure of the encoded classifiers • Experimental results point out: – Olex-GA quickly converges to very accurate classifiers; – Olex-GA performs at a competitive level with standard algorithms; – Time efficiency is lower than Olex-Greedy but higher than the other rule learning methods, such as Ripper and C45. A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction UNIVERSITA’ DELLA CALABRIA Future work Dipartimento di MATEMATICA • Extension of the proposed technique to deal with classifiers of the form c ( T1 d ... Tn d ) ( Tn 1 d ... Tn m d ) where each Ti is a conjunction of “simple” terms: Ti ti .... tik 1 A.Pietramala, V.Policicchio, P.Rullo, I. Sidhu A Genetic Algorithm for Text Classification Rule Induction

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