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Conference

Mexican International Conference on Artificial Intelligence 

About: Mexican International Conference on Artificial Intelligence is an academic conference. The conference publishes majorly in the area(s): Artificial neural network & Fuzzy logic. Over the lifetime, 2090 publications have been published by the conference receiving 12174 citations.


Papers
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Book ChapterDOI
26 Apr 2004
TL;DR: This work develops a systematic study aiming to question whether class imbalances are truly to blame for the loss of performance of learning systems or whether the class imbalance are not a problem by themselves.
Abstract: Several works point out class imbalance as an obstacle on applying machine learning algorithms to real world domains. However, in some cases, learning algorithms perform well on several imbalanced domains. Thus, it does not seem fair to directly correlate class imbalance to the loss of performance of learning algorithms. In this work, we develop a systematic study aiming to question whether class imbalances are truly to blame for the loss of performance of learning systems or whether the class imbalances are not a problem by themselves. Our experiments suggest that the problem is not directly caused by class imbalances, but is also related to the degree of overlapping among the classes.

337 citations

Book ChapterDOI
26 Apr 2004
TL;DR: A parallel version of a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts and indicates that both parallel versions produce important improvements in the execution times of the algorithm (with respect to the serial version) while keeping the quality of the results obtained.
Abstract: In this paper, we present a parallel version of a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts. Such an algorithm was previosly developed by the authors. Two approaches were adopted to parallelize our algorithm (both of them based on a master-slave scheme): one uses Pthreads (shared memory) and the other one uses MPI (distributed memory). We conduct a small comparative study to analyze the impact that the parallelization has on performance. Our results indicate that both parallel versions produce important improvements in the execution times of the algorithm (with respect to the serial version) while keeping the quality of the results obtained.

247 citations

Book ChapterDOI
14 Nov 2005
TL;DR: An evolutionary-based approach to solve engineering design problems without using penalty functions is proposed and tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems.
Abstract: We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. The results obtained are discussed and some conclusions are provided.

188 citations

Book ChapterDOI
27 Oct 2012
TL;DR: This paper examines how classifiers work while doing opinion mining over Spanish Twitter data, and presents best settings of parameters for practical applications of opinion mining in Spanish Twitter.
Abstract: Opinion mining deals with determining of the sentiment orientation--positive, negative, or neutral--of a (short) text. Recently, it has attracted great interest both in academia and in industry due to its useful potential applications. One of the most promising applications is analysis of opinions in social networks. In this paper, we examine how classifiers work while doing opinion mining over Spanish Twitter data. We explore how different settings (n-gram size, corpus size, number of sentiment classes, balanced vs. unbalanced corpus, various domains) affect precision of the machine learning algorithms. We experimented with Naive Bayes, Decision Tree, and Support Vector Machines. We describe also language specific preprocessing--in our case, for Spanish language--of tweets. The paper presents best settings of parameters for practical applications of opinion mining in Spanish Twitter. We also present a novel resource for analysis of emotions in texts: a dictionary marked with probabilities to express one of the six basic emotions(Probability Factor of Affective use (PFA)(Spanish Emotion Lexicon that contains 2,036 words.

137 citations

Book ChapterDOI
25 Oct 2015
TL;DR: An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d\(^2\)HUP, HUI-Miner, HUP-M Miner, FHM and UP-Growth+.
Abstract: High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d\(^2\)HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.

136 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202265
202158
202077
201959
201877
201770