Wild Mushrooms Classification – Edible Or Poisonous

ECE539 Project Proposal
Yulin Shen
11/14/2013
Wild Mushrooms Classification – Edible Or Poisonous
Mushrooms, as a kind of food, are very special due to their edibility. Some countries, for
example, China, treat mushrooms as high nutrition food. However, only small portions of
them are really ate by people. And also some explorers may get lost in some forests
without food. They can eat mushrooms, however, they do not know whether they are
edible. Hence, I want to use some classification algorithms to develop a best model to
predict whether new emerging mushrooms are edible based on the detected data about
mushrooms (features and edibility).
In order to establish a model for predicting edibility of mushrooms, a dataset with enough
features is necessary. In the UCI Machine Learning Repository, there is a dataset, which
has 8124 instances with 22 attributes and the edibility. However, these 23 kinds of data
are descriptive, not numerical. So, I need to preprocess the data. For example, the “edible”
can be 1, and “poisonous” be 0, and if an attribute has 5 choices, I would use 5 bits
strings to replace them. Because I just have a dataset, I intend to split the dataset into 2
files. One is for training data, and another is testing data. The best way is I can get
another testing dataset. After getting the numerical data with same meaning, I need to
consider about the algorithms for classification. In the class, I have already learned K-

Nearest Neighbor Classifier, Multi-Layer Perceptron, and other algorithms. In the project,
I want to make some models based on the algorithms I have already learned in the class.
Furthermore, I want to implement another algorithm outside the class.
There is a reference teaches us top 10 algorithms for data mining. Although some of them
are mentioned in the class, I can implement 1 algorithm to do comparison. And also there
is an efficient clustering algorithm for categorical dataset that is K-Histograms. My aim
in the project is to make an accurate model for predicting the edibility of mushrooms.
Hence, my plan is to better understand the classification algorithms and looking for more
data to do testing. Classification and prediction are really useful, even in other fields.
Reference:
1. http://arxiv.org/pdf/cs/0509033.pdf (K-Histograms)
2. http://www.cs.umd.edu/~samir/498/10Algorithms-08.pdf (Top 10 algorithms for
data mining)
3. http://archive.ics.uci.edu/ml/datasets/Mushroom (Dataset)