improve weka classifier results - weka

Question

I have a database which consists of 27 attributes and 597 instances .
I want to classify it with as best results as possible using Weka.
Which classifier is not important .The class attribute is nominal and the rest are numeric .
The Best results until now was LWL (83.2215) and oneR(83.389). I used attribute selection filter but the results are not improved and no other classifier can give better results even NN or SMO or meta classes.
Any idea about how to improve this database knowing that there are no missing values and the database is about 597 patients gathered in three years.

Answer 1

Have you tried boosting or bagging? These generally can help improve results.
http://machinelearningmastery.com/improve-machine-learning-results-with-boosting-bagging-and-blending-ensemble-methods-in-weka/
Boosting
Boosting is an ensemble method that starts out with a base classifier
that is prepared on the training data. A second classifier is then
created behind it to focus on the instances in the training data that
the first classifier got wrong. The process continues to add
classifiers until a limit is reached in the number of models or
accuracy.
Boosting is provided in Weka in the AdaBoostM1 (adaptive boosting)
algorithm.
Click “Add new…” in the “Algorithms” section. Click the “Choose”
button. Click “AdaBoostM1” under the “meta” selection. Click the
“Choose” button for the “classifier” and select “J48” under the “tree”
section and click the “choose” button. Click the “OK” button on the
“AdaBoostM1” configuration.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble method that creates
separate samples of the training dataset and creates a classifier for
each sample. The results of these multiple classifiers are then
combined (such as averaged or majority voting). The trick is that each
sample of the training dataset is different, giving each classifier
that is trained, a subtly different focus and perspective on the
problem.
Click “Add new…” in the “Algorithms” section. Click the “Choose”
button. Click “Bagging” under the “meta” selection. Click the “Choose”
button for the “classifier” and select “J48” under the “tree” section
and click the “choose” button. Click the “OK” button on the “Bagging”
configuration.

Answer 2

I tried Boosting and Bagging as #applecrusher has mentioned. It showed a little improvement in the accuracy; but for the same data with SKLearn, I was getting a lot better accuracy. When I compared the code and output at each step, I found that train-test split function in SKLearn was, by default, shuffling the data. When I shuffled the data for WEKA using Collections.shuffle(), I saw improved results. Give it a try.