OPTIMASI ALGORITMA C4.5 BERBASIS PARTICLE SWARM OPTIMIZATION (PSO) UNTUK MENENTUKAN WHOLESALES PENJUALAN

Algorithm C4.5 is an algorithm used to form a Decision Tree. The C4.5 Algorithm definitely has advantages and disadvantages. The features of the C4.5 algorithm can create decision trees that are easy to version, dominate the level of acceptable accuracy, efficient in managing effective category attributes and can set attributes of discrete and numeric types, and in an advantage there are definitely drawbacks. The weakness of the C4.5 algorithm is the instability in determining accuracy. The amount of data used is 1000 with 7 attributes. Data were analyzed using Particle Swarm optimization in C4.5. Because the accuracy produced by C4.5 is still low, it is optimized with Particle Swarm optimization. Accuracy on C4.5 is 81% after using optimization the accuracy increases by 86%. Data processing uses Python programming and accuracy testing uses the Confusion Matrix to compare accuracy results

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Using classifiers to predict linear feedback shift registers

Proceeding of: IEEE 35th International Carnahan Conference on Security Technology. October 16-19, 2001, LondonPreviously (J.C. Hernandez et al., 2000), some new ideas that justify the use of artificial intelligence techniques in cryptanalysis are presented. The main objective of that paper was to show that the theoretical next bit prediction problem can be transformed into a classification problem, and this classification problem could be solved with the aid of some AI algorithms. In particular, they showed how a well-known classifier called c4.5 could predict the next bit generated by a linear feedback shift register (LFSR, a widely used model of pseudorandom number generator) very efficiently and, most importantly, without any previous knowledge over the model used. The authors look for other classifiers, apart from c4.5, that could be useful in the prediction of LFSRs. We conclude that the selection of c4.5 by Hernandez et al. was adequate, because it shows the best accuracy of all the classifiers tested. However, we have found other classifiers that produce interesting results, and we suggest that these algorithms must be taken into account in the future when trying to predict more complex LFSR-based models. Finally, we show some other properties that make the c4.5 algorithm the best choice for this particular cryptanalytic problem.Publicad

Hierarchical meta-rules for scalable meta-learning

The Pairwise Meta-Rules (PMR) method proposed in [18] has been shown to improve the predictive performances of several metalearning algorithms for the algorithm ranking problem. Given m target objects (e.g., algorithms), the training complexity of the PMR method with respect to m is quadratic: (formula presented). This is usually not a problem when m is moderate, such as when ranking 20 different learning algorithms. However, for problems with a much larger m, such as the meta-learning-based parameter ranking problem, where m can be 100+, the PMR method is less efficient. In this paper, we propose a novel method named Hierarchical Meta-Rules (HMR), which is based on the theory of orthogonal contrasts. The proposed HMR method has a linear training complexity with respect to m, providing a way of dealing with a large number of objects that the PMR method cannot handle efficiently. Our experimental results demonstrate the benefit of the new method in the context of meta-learning

Automatic correction of part-of-speech corpora

In this study a simple method for automatic correction of part-ofspeech corpora is presented, which works as follows: Initially two or more already available part-of-speech taggers are applied on the data. Then a sample of differing outputs is taken to train a classifier to predict for each difference which of the taggers (if any) delivered the correct output. As classifiers we employed instance-based learning, a C4.5 decision tree and a Bayesian classifier. Their performances ranged from 59.1 % to 67.3 %. Training on the automatically corrected data finally lead to significant improvements in tagger performance

A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces

In this paper we present first results from a comparative study. Its aim is to test the feasibility of different inductive learning techniques to perform the automatic acquisition of linguistic knowledge within a natural language database interface. In our interface architecture the machine learning module replaces an elaborate semantic analysis component. The learning module learns the correct mapping of a user's input to the corresponding database command based on a collection of past input data. We use an existing interface to a production planning and control system as evaluation and compare the results achieved by different instance-based and model-based learning algorithms.Comment: 10 pages, to appear CoNLL9