By Grigori Sidorov, Sabino Miranda-Jiménez, Francisco Viveros-Jiménez, Alexander Gelbukh (auth.), Ildar Batyrshin, Miguel González Mendoza (eds.)
The two-volume set LNAI 7629 and LNAI 7630 constitutes the refereed lawsuits of the eleventh Mexican overseas convention on synthetic Intelligence, MICAI 2012, held in San Luis Potosí, Mexico, in October/November 2012. The eighty revised papers provided have been rigorously reviewed and chosen from 224 submissions. the 1st quantity contains forty papers representing the present major themes of curiosity for the AI group and their functions. The papers are equipped within the following topical sections: desktop studying and trend attractiveness; desktop imaginative and prescient and snapshot processing; robotics; wisdom illustration, reasoning, and scheduling; clinical functions of man-made intelligence.
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Additional resources for Advances in Artificial Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part I
We tried six different sizes. Results are shown in Table 3. It confirms that unigram is the best feature size. This conclusion confirms the conclusions obtained in other studies for English language and different corpus domain such as Twitter and films reviews [4, 9]. Table 3. 3 N-gram size 1 2 3 4 5 6 Naïve Bayes 46 37 35 35 35 35 J48 57 41 35 35 35 35 SVM 61 49 41 35 35 35 Effect of the Number of Classes Table 4 describes the values of the number of classes and their composition. For example, in class number 2 there are two types of categories: positive and negative, but positive value also corresponds to positive, neutral, and news opinions.
Negative III. neutral IV. news 4 Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets 9 Table 5. 0 Table 5 shows the effect of the number of classes on the classifier performance. We see that reducing the number of classes increases the classifiers precision. It is not surprising because we decrease the possibility of errors. 4 Effect of Balanced vs. Unbalanced Corpus In this section, our goal was to analyze the effect of balanced vs. unbalanced corpus on classification.
Div. W. 0385 The Pima Indian diabetes dataset consists of 768 entities, eight numerical features and two clusters. In terms of cardinality, the clusters have 500 and 268 entities. An update recently (28/02/2011) posted on the web page regarding this dataset , stating that some of its values are biologically implausible; however, An Empirical Evaluation of Diﬀerent Initializations 21 we decided to keep the dataset unchanged for easy comparison with previously published papers. The results of this dataset can be found in Table 5.