Reducing the dimensionality of a model parameter space, this strategy enables to explore the space in more detail. The other strategy that can be thought of is refining the ensemble by discarding models which use weak attributes. We expect that such refinement can improve the BMA performance.
To test the assumption made in section 2 and refine DT model ensembles obtained with BMA, we propose a new strategy aiming at discarding the DT models which use weak attributes. According to this strategy, first the BMA technique described in section 2 is used to collect DT models. Then posterior probabilities of using attributes in the ensemble of DT models are estimated. These estimates give us the posterior information on feature importance. Having obtained a range of the posterior probabilities, we then define a threshold value to cut off the attributes with the probabilities below this threshold – we define such attributes as weak. At the next stage we find the DT models which use these weak attributes and finally discard these DT models from the ensemble.
Obviously, the larger the threshold value, the greater number of attributes is defined as weak, and therefore the larger portion of DT models is discarded. The efficiency of this discarding technique is evaluated in terms of the accuracy of the refined DT ensemble on the test data. The uncertainty in the ensemble outcomes is evaluated in terms of entropy. Having a set of the threshold probability values obtained in a series of experiments, we can expect that there is an optimal threshold value at which the performance becomes higher. We can also expect to find a threshold value at which the uncertainty becomes lower. In the following section we test the proposed technique on the p...
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...hreshold is gradually increased from 0.0 to 0.005. At the same time the uncertainty in decisions is decreased from 478.4 to 469.0 in terms of entropy E of the ensemble. For comparison, we applied a technique of discarding the same weak attributes and then reran the BMA on the data reduced in their dimensionality.
From Table 1 we can see that the BMA performance has slightly increased from 27.4 to 29.0 when 23 weak attributes were discarded. The discarding of 31 attribute has resulted in a decrease in the ensemble entropy from 478.3 to 463.6. Overall, the both techniques are shown to provide the comparable performance and ensemble entropy. However, the technique of discarding attributes has shown to tend to perform in a larger variation. Within this technique for each threshold value it is required to retrain DT ensemble on the data of a new dimensionality.
I do not predict that all of my results will follow a line of best fit
By the second week we had had a few complications with the server, which led to us not being able to attain all our sprint goals for the week, thus did not have anything to demo and came to the realization that we might need an alternate solution. By this period, we also had difficulties starting out with developing our ACC since no data could be obtained from the sensor and we were suggested to continue our development and in the need of data, send fake data until further notice.
Accuracy: This paper demonstrates much accuracy, this is proven through the subtitles, statistics and in text citations for
While much of this course covered learning methods of most efficiently performing certain tasks, these last two weeks have been focused on being able to identify if you have most efficiently implemented such an algorithm. The skill I’ve learned this week is to determine when improvements have, very likely, reached their maximum and how to show that they have.
Society selectively chooses one’s legacy. No one chooses to remember Hitler for his efforts to preserve wildlife. Everyone remembers him for WWII and The Holocaust. But Asoka’s legacy, leader of the Mauryan Empire (located in modern India) from 268-232 BCE, is not so obvious. He was the founding father of India and brilliantly built the Mauryan Empire into a world power. But he also had a dark side, causing debate about his legacy. Asoka was an enlightened ruler because he added Kalinga to India, made many reforms, and promoted welfare.
We can rationalize not using DCF for its inability to capture risk uncertainty. Passive investments such as stocks and bonds are good candidates to use DCF on. Once these investments are made, investors cannot influence the cash flow generation. We agree that decision tree can be used to make preliminary judgments and real option analysis can be used to get more definitive answers. We think that sensitivity analysis and scenario analysis could be useful since all inputs may change over time.
At the beginning of the Glo Bus simulation our team developed a strategy that would allow us to compete with the other seven teams and put us in a position to gain a competitive edge. During the first few phases we were able to give a strong showing, however as the game progressed it was clear that other teams were implementing a similar strategy and making headway into our space. Reacting to the market changes was showing negative effects on our growth so we developed a new strategy to set us apart and allow our company to compete.
Let us now see the quality of individual the population over the time. As shown below at the starting point of the algorithm individuals are of less quality. However as the time goes by population’s individuals are getting of higher quality and reaching the pick of global and local optima. The image below illustrate these stages of the algorithm.
In order to encompass and fully grasp the meaning and structure of a song, one must be able to analyze the song through different spectrums. For instance, every song is produced differently in order to send out a specific message or stand for a certain issue. At times, when music is produced, it embodies a significant attitude towards its audience in which they are able to sense the quality and atmosphere of the song. This essay will be analyzing the musical, textual, and visual evidence of “Sur ma route” by Black M who is known as one of the top current “rappers” in French popular music.
The MDA model also showed potential to ease some problems in the selection of securities for a portfolio, but further investigation was recommended.
For advertisers, Data Mining can turn into a valuable tool with the emerging new media of internet, blogs, podcasts and search ads (as opposed to the traditional media, such as television, radio, or newspapers). The incre...
Future research is needed to compare the classification abilities of this method in various situations with other case-based classification methods is needed to see some other result how to evaluate the customers review by using DRSA method and 4emka2 software decision result.
... applied on different Domain data sets and sub level data sets. The data sets are applied on Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms, I got 60-70% of accuracy. The above is also applied for the Unigrams of Maximum entropy, Support Vector Machine Method, Multinomial naïve bayes algorithms achieved an accuracy of 65-75%. Applied the same data on proposed lexicon Based Semantic Orientation Analysis Algorithm, we received better accuracy of 85%. In subjective Feature Relation Networks Chi-square model using n-grams, POS tagging by applying linguistic rules performed with highest accuracy of 80% to 93% significantly better than traditional naïve bayes with unigram model. The after applying proposed model on different sets the results are validated with test data and proved our methods are more accurate than the other methods.
Smith, C. L., Calkins, S. D., Keane,S. P., Anastopoulos, A. D., Shelton, T. L. (2004). Predicting
T. Mitchell, Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Draft Version, 2005 download